Authentication Services Guide
Build trusted card authentication and grading services using the Trading Card API. This guide covers verification workflows, fraud detection, digital certification, and integration with professional grading services.
Prerequisites
- Trading Card API access credentials
- Image processing capabilities
- Database for authentication records
- Understanding of card grading standards
- Knowledge of security best practices
Architecture Overview
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Submission │ │ Authentication │ │ Certificate │
│ Portal │ │ Service │ │ Generation │
│ │ │ │ │ │
│ - Card Upload │───►│ - Verification │───►│ - Digital Cert │
│ - Metadata │ │ - Grading │ │ - Blockchain │
│ - Payment │ │ - Fraud Check │ │ - Registry │
│ │ │ - Quality Assure│ │ │
└─────────────────┘ └──────────────────┘ └─────────────────┘
Step 1: Card Identification and Verification
Advanced Card Matching System
import cv2
import numpy as np
from PIL import Image
import imagehash
import requests
from typing import Dict, List, Tuple, Optional
class CardIdentificationService:
def __init__(self, api_client):
self.api_client = api_client
self.image_cache = {}
def identify_card_from_image(self, image_path: str, confidence_threshold: float = 0.8) -> Dict:
"""Identify a card using computer vision and API matching"""
# Load and preprocess image
card_image = cv2.imread(image_path)
processed_image = self.preprocess_card_image(card_image)
# Extract features
features = self.extract_card_features(processed_image)
# Search API for potential matches
potential_matches = self.search_potential_matches(features)
# Perform detailed matching
best_matches = []
for candidate in potential_matches:
match_score = self.calculate_match_score(processed_image, candidate)
if match_score >= confidence_threshold:
best_matches.append({
"card_id": candidate["id"],
"match_score": match_score,
"card_data": candidate,
"verification_details": self.get_verification_details(candidate)
})
# Sort by match score
best_matches.sort(key=lambda x: x["match_score"], reverse=True)
return {
"identification_success": len(best_matches) > 0,
"top_match": best_matches[0] if best_matches else None,
"all_matches": best_matches,
"confidence_scores": [m["match_score"] for m in best_matches],
"verification_timestamp": datetime.now().isoformat()
}
def preprocess_card_image(self, image: np.ndarray) -> np.ndarray:
"""Preprocess card image for better matching"""
# Convert to RGB
if len(image.shape) == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Detect card boundaries
card_contour = self.detect_card_boundaries(image)
if card_contour is not None:
# Perspective correction
image = self.correct_perspective(image, card_contour)
# Normalize lighting
image = self.normalize_lighting(image)
# Standard size
image = cv2.resize(image, (400, 560)) # Standard card aspect ratio
return image
def detect_card_boundaries(self, image: np.ndarray) -> Optional[np.ndarray]:
"""Detect card boundaries using edge detection"""
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
edges = cv2.Canny(blurred, 50, 150)
# Find contours
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Find largest rectangular contour
for contour in sorted(contours, key=cv2.contourArea, reverse=True):
# Approximate contour
epsilon = 0.02 * cv2.arcLength(contour, True)
approx = cv2.approxPolyDP(contour, epsilon, True)
# If we found a quadrilateral
if len(approx) == 4:
return approx
return None
def extract_card_features(self, image: np.ndarray) -> Dict:
"""Extract distinctive features from card image"""
# Convert to PIL for hashing
pil_image = Image.fromarray(image)
# Generate perceptual hashes
features = {
"dhash": str(imagehash.dhash(pil_image)),
"phash": str(imagehash.phash(pil_image)),
"ahash": str(imagehash.average_hash(pil_image)),
"whash": str(imagehash.whash(pil_image)),
}
# Extract color histogram
hist_b = cv2.calcHist([image], [0], None, [32], [0, 256])
hist_g = cv2.calcHist([image], [1], None, [32], [0, 256])
hist_r = cv2.calcHist([image], [2], None, [32], [0, 256])
features["color_histogram"] = {
"red": hist_r.flatten().tolist(),
"green": hist_g.flatten().tolist(),
"blue": hist_b.flatten().tolist()
}
# Extract edge features
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
edges = cv2.Canny(gray, 100, 200)
edge_density = np.sum(edges > 0) / (edges.shape[0] * edges.shape[1])
features["edge_density"] = edge_density
return features
def search_potential_matches(self, features: Dict) -> List[Dict]:
"""Search API for cards with similar features"""
# Use perceptual hashes to find similar images
search_params = {
"filter[image_hash]": features["phash"],
"filter[similarity_threshold]": 0.7,
"include": "set,player,authentication_records",
"page[limit]": 50
}
response = self.api_client.get("/cards/search-by-image", params=search_params)
if response.status_code == 200:
return response.json()["data"]
else:
# Fallback to metadata-based search
return self.fallback_metadata_search(features)
def calculate_match_score(self, query_image: np.ndarray, candidate: Dict) -> float:
"""Calculate detailed match score between images"""
# Download candidate image
candidate_image = self.download_and_process_image(candidate["attributes"]["image_url"])
if candidate_image is None:
return 0.0
# Multiple comparison metrics
scores = []
# Structural similarity
ssim_score = self.calculate_ssim(query_image, candidate_image)
scores.append(("ssim", ssim_score, 0.4))
# Histogram comparison
hist_score = self.compare_histograms(query_image, candidate_image)
scores.append(("histogram", hist_score, 0.3))
# Feature matching
feature_score = self.compare_keypoint_features(query_image, candidate_image)
scores.append(("features", feature_score, 0.3))
# Weighted average
total_score = sum(score * weight for _, score, weight in scores)
return min(total_score, 1.0) # Cap at 1.0
def get_verification_details(self, card_data: Dict) -> Dict:
"""Get comprehensive verification details for a card"""
card_id = card_data["id"]
attrs = card_data["attributes"]
# Check authentication history
auth_history = self.api_client.get(f"/cards/{card_id}/authentication-history")
# Get market data for verification
market_data = self.api_client.get(f"/cards/{card_id}/market-data")
# Compile verification details
return {
"card_specifications": {
"name": attrs.get("name"),
"year": attrs.get("year"),
"set_name": attrs.get("set_name"),
"number": attrs.get("number"),
"manufacturer": attrs.get("manufacturer")
},
"authentication_history": auth_history.json().get("data", []),
"known_variations": attrs.get("variations", []),
"market_indicators": {
"current_value_range": market_data.json().get("value_range"),
"authentication_premium": market_data.json().get("auth_premium"),
"recent_sales": market_data.json().get("recent_sales", [])
},
"risk_indicators": self.assess_fraud_risk(card_data)
}
# Usage example
identifier = CardIdentificationService(api_client)
result = identifier.identify_card_from_image("uploaded_card.jpg")
if result["identification_success"]:
top_match = result["top_match"]
print(f"Card identified: {top_match['card_data']['attributes']['name']}")
print(f"Confidence: {top_match['match_score']:.2%}")
else:
print("Card could not be identified with confidence")
Step 2: Professional Grading Service Integration
PSA Integration
class PSAGradingIntegration:
def __init__(self, psa_credentials: Dict, api_client):
self.psa_api_key = psa_credentials["api_key"]
self.psa_base_url = "https://api.psacard.com"
self.api_client = api_client
def submit_for_grading(self, card_id: str, submission_details: Dict) -> Dict:
"""Submit card to PSA for professional grading"""
# Get card details from Trading Card API
card_response = self.api_client.get(f"/cards/{card_id}")
card_data = card_response.json()["data"]
# Prepare PSA submission
psa_submission = {
"submission_type": "grading",
"service_level": submission_details.get("service_level", "regular"),
"declared_value": submission_details.get("declared_value", 100),
"cards": [{
"card_description": self.format_card_description(card_data),
"year": card_data["attributes"]["year"],
"brand": card_data["attributes"].get("manufacturer", "Unknown"),
"card_number": card_data["attributes"].get("number"),
"player_name": card_data["attributes"].get("player_name"),
"auto_grade": submission_details.get("auto_grade", False),
"trading_card_api_id": card_id
}]
}
# Submit to PSA
headers = {
"Authorization": f"Bearer {self.psa_api_key}",
"Content-Type": "application/json"
}
response = requests.post(
f"{self.psa_base_url}/submissions",
json=psa_submission,
headers=headers
)
if response.status_code == 201:
psa_data = response.json()
# Record submission in our system
self.record_grading_submission(card_id, psa_data, "PSA")
return {
"success": True,
"psa_submission_id": psa_data["submission_id"],
"tracking_number": psa_data["tracking_number"],
"estimated_completion": psa_data["estimated_completion_date"],
"total_cost": psa_data["total_cost"]
}
else:
return {
"success": False,
"error": response.json().get("message", "Submission failed")
}
def check_grading_status(self, psa_submission_id: str) -> Dict:
"""Check PSA grading status"""
headers = {"Authorization": f"Bearer {self.psa_api_key}"}
response = requests.get(
f"{self.psa_base_url}/submissions/{psa_submission_id}",
headers=headers
)
if response.status_code == 200:
psa_data = response.json()
# Update our records
self.update_grading_status(psa_submission_id, psa_data)
return {
"status": psa_data["status"],
"current_stage": psa_data["current_stage"],
"grade_assigned": psa_data.get("grade"),
"cert_number": psa_data.get("cert_number"),
"estimated_completion": psa_data.get("estimated_completion"),
"notes": psa_data.get("grader_notes", [])
}
else:
return {"error": "Failed to retrieve status"}
def format_card_description(self, card_data: Dict) -> str:
"""Format card description for PSA submission"""
attrs = card_data["attributes"]
parts = []
if attrs.get("year"):
parts.append(str(attrs["year"]))
if attrs.get("manufacturer"):
parts.append(attrs["manufacturer"])
if attrs.get("set_name"):
parts.append(attrs["set_name"])
if attrs.get("name"):
parts.append(attrs["name"])
if attrs.get("number"):
parts.append(f"#{attrs['number']}")
return " ".join(parts)
def record_grading_submission(self, card_id: str, psa_data: Dict, service: str) -> None:
"""Record grading submission in Trading Card API"""
submission_record = {
"data": {
"type": "authentication_records",
"attributes": {
"card_id": card_id,
"service_provider": service,
"submission_id": psa_data["submission_id"],
"submission_date": datetime.now().isoformat(),
"status": "submitted",
"tracking_number": psa_data.get("tracking_number"),
"declared_value": psa_data.get("declared_value"),
"service_level": psa_data.get("service_level")
}
}
}
self.api_client.post("/authentication-records", json=submission_record)
BGS Integration
class BGSGradingIntegration:
def __init__(self, bgs_credentials: Dict, api_client):
self.bgs_username = bgs_credentials["username"]
self.bgs_password = bgs_credentials["password"]
self.bgs_base_url = "https://api.beckett.com"
self.api_client = api_client
def submit_for_grading(self, card_id: str, submission_details: Dict) -> Dict:
"""Submit card to BGS for grading"""
# BGS authentication
auth_response = requests.post(f"{self.bgs_base_url}/auth", json={
"username": self.bgs_username,
"password": self.bgs_password
})
if auth_response.status_code != 200:
return {"success": False, "error": "BGS authentication failed"}
bgs_token = auth_response.json()["access_token"]
# Get card details
card_response = self.api_client.get(f"/cards/{card_id}")
card_data = card_response.json()["data"]
# Prepare BGS submission
bgs_submission = {
"submitter_info": {
"account_number": submission_details["account_number"],
"contact_info": submission_details["contact_info"]
},
"cards": [{
"description": self.format_bgs_description(card_data),
"declared_value": submission_details.get("declared_value", 100),
"grading_options": {
"autograph_authentication": submission_details.get("auto_auth", False),
"subgrades": submission_details.get("subgrades", True),
"card_saver": submission_details.get("card_saver", False)
}
}],
"service_level": submission_details.get("service_level", "economy")
}
headers = {
"Authorization": f"Bearer {bgs_token}",
"Content-Type": "application/json"
}
response = requests.post(
f"{self.bgs_base_url}/submissions",
json=bgs_submission,
headers=headers
)
if response.status_code == 201:
bgs_data = response.json()
self.record_grading_submission(card_id, bgs_data, "BGS")
return {
"success": True,
"bgs_submission_id": bgs_data["submission_id"],
"estimated_completion": bgs_data["estimated_turnaround"],
"total_cost": bgs_data["total_fees"]
}
else:
return {"success": False, "error": "BGS submission failed"}
def format_bgs_description(self, card_data: Dict) -> str:
"""Format card description for BGS submission"""
attrs = card_data["attributes"]
description_parts = [
attrs.get("year", ""),
attrs.get("manufacturer", ""),
attrs.get("set_name", ""),
attrs.get("name", ""),
f"#{attrs.get('number', '')}" if attrs.get("number") else ""
]
return " ".join(filter(None, description_parts))
Step 3: Fraud Detection and Prevention
Advanced Fraud Detection System
import tensorflow as tf
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler
import joblib
class FraudDetectionService:
def __init__(self, api_client):
self.api_client = api_client
self.fraud_model = None
self.scaler = StandardScaler()
self.load_fraud_model()
def load_fraud_model(self) -> None:
"""Load pre-trained fraud detection model"""
try:
self.fraud_model = joblib.load("fraud_detection_model.pkl")
self.scaler = joblib.load("fraud_scaler.pkl")
except FileNotFoundError:
print("Fraud detection model not found, training new model...")
self.train_fraud_model()
def assess_fraud_risk(self, card_data: Dict, submission_data: Dict) -> Dict:
"""Comprehensive fraud risk assessment"""
# Extract features for fraud detection
features = self.extract_fraud_features(card_data, submission_data)
# Multiple fraud detection methods
risk_scores = {}
# 1. Statistical anomaly detection
risk_scores["statistical_anomaly"] = self.detect_statistical_anomalies(features)
# 2. Image authenticity analysis
risk_scores["image_authenticity"] = self.analyze_image_authenticity(
submission_data.get("image_path")
)
# 3. Historical pattern analysis
risk_scores["historical_patterns"] = self.analyze_historical_patterns(card_data)
# 4. Market value consistency
risk_scores["market_consistency"] = self.check_market_consistency(
card_data, submission_data
)
# 5. Submission metadata analysis
risk_scores["metadata_analysis"] = self.analyze_submission_metadata(submission_data)
# Calculate overall risk score
weights = {
"statistical_anomaly": 0.25,
"image_authenticity": 0.30,
"historical_patterns": 0.20,
"market_consistency": 0.15,
"metadata_analysis": 0.10
}
overall_risk = sum(
risk_scores[metric] * weights[metric]
for metric in weights.keys()
)
# Determine risk level
if overall_risk >= 0.8:
risk_level = "HIGH"
recommendation = "REJECT"
elif overall_risk >= 0.6:
risk_level = "MEDIUM"
recommendation = "MANUAL_REVIEW"
elif overall_risk >= 0.3:
risk_level = "LOW"
recommendation = "ENHANCED_VERIFICATION"
else:
risk_level = "MINIMAL"
recommendation = "PROCEED"
return {
"overall_risk_score": overall_risk,
"risk_level": risk_level,
"recommendation": recommendation,
"individual_scores": risk_scores,
"risk_factors": self.identify_risk_factors(risk_scores),
"assessment_timestamp": datetime.now().isoformat()
}
def detect_statistical_anomalies(self, features: Dict) -> float:
"""Detect statistical anomalies in card features"""
if self.fraud_model is None:
return 0.5 # Neutral score if no model
# Prepare feature vector
feature_vector = np.array([
features.get("declared_value", 0),
features.get("image_quality_score", 0),
features.get("metadata_completeness", 0),
features.get("submission_urgency", 0),
features.get("user_history_score", 0)
]).reshape(1, -1)
# Scale features
feature_vector_scaled = self.scaler.transform(feature_vector)
# Get anomaly score (-1 for outliers, 1 for inliers)
anomaly_score = self.fraud_model.decision_function(feature_vector_scaled)[0]
# Convert to risk score (0-1, where 1 is highest risk)
risk_score = max(0, (1 - anomaly_score) / 2)
return risk_score
def analyze_image_authenticity(self, image_path: str) -> float:
"""Analyze image for signs of manipulation or forgery"""
if not image_path:
return 0.8 # High risk if no image provided
image = cv2.imread(image_path)
risk_indicators = []
# 1. Image quality analysis
quality_score = self.assess_image_quality(image)
if quality_score < 0.3:
risk_indicators.append("poor_image_quality")
# 2. Digital manipulation detection
manipulation_score = self.detect_digital_manipulation(image)
if manipulation_score > 0.6:
risk_indicators.append("potential_manipulation")
# 3. Printing pattern analysis
print_authenticity = self.analyze_printing_patterns(image)
if print_authenticity < 0.4:
risk_indicators.append("suspicious_printing")
# 4. Metadata analysis
metadata_score = self.analyze_image_metadata(image_path)
if metadata_score > 0.7:
risk_indicators.append("metadata_anomalies")
# Calculate composite risk score
base_risk = len(risk_indicators) * 0.25
quality_risk = 1 - quality_score
manipulation_risk = manipulation_score
total_risk = min(1.0, (base_risk + quality_risk + manipulation_risk) / 3)
return total_risk
def check_market_consistency(self, card_data: Dict, submission_data: Dict) -> float:
"""Check if declared value is consistent with market data"""
card_id = card_data["id"]
declared_value = submission_data.get("declared_value", 0)
# Get recent market data
market_response = self.api_client.get(f"/cards/{card_id}/market-summary")
if market_response.status_code != 200:
return 0.5 # Neutral if no market data
market_data = market_response.json()["data"]
# Extract market value ranges by condition
market_ranges = market_data.get("value_ranges", {})
condition = submission_data.get("estimated_condition", "near_mint")
if condition not in market_ranges:
return 0.6 # Slightly elevated risk
market_range = market_ranges[condition]
low_value = market_range["low"]
high_value = market_range["high"]
median_value = market_range["median"]
# Calculate value consistency score
if declared_value < low_value * 0.5:
return 0.9 # Very high risk - severely undervalued
elif declared_value > high_value * 3:
return 0.8 # High risk - severely overvalued
elif low_value <= declared_value <= high_value:
return 0.1 # Low risk - within market range
elif declared_value > high_value:
deviation = (declared_value - median_value) / median_value
return min(0.7, deviation * 0.5) # Escalating risk for overvaluation
else:
deviation = (median_value - declared_value) / median_value
return min(0.6, deviation * 0.3) # Moderate risk for undervaluation
# Blockchain authentication integration
class BlockchainAuthenticationService:
def __init__(self, blockchain_config: Dict):
self.blockchain_config = blockchain_config
self.contract_address = blockchain_config["contract_address"]
self.private_key = blockchain_config["private_key"]
def create_authentication_certificate(self, card_id: str,
grading_result: Dict) -> Dict:
"""Create immutable authentication certificate on blockchain"""
from web3 import Web3
# Connect to blockchain network
w3 = Web3(Web3.HTTPProvider(self.blockchain_config["rpc_url"]))
# Prepare certificate data
certificate_data = {
"card_id": card_id,
"grade": grading_result.get("grade"),
"condition": grading_result.get("condition"),
"authentication_date": datetime.now().isoformat(),
"grading_service": grading_result.get("service"),
"cert_number": grading_result.get("cert_number"),
"grader_notes": grading_result.get("notes", ""),
"image_hash": grading_result.get("image_hash"),
"authenticity_score": grading_result.get("authenticity_score", 1.0)
}
# Create certificate hash
certificate_hash = self.create_certificate_hash(certificate_data)
# Submit to blockchain
try:
# Load smart contract
contract = w3.eth.contract(
address=self.contract_address,
abi=self.load_contract_abi()
)
# Build transaction
transaction = contract.functions.createAuthenticationCertificate(
card_id,
certificate_hash,
int(grading_result.get("grade", 0)),
grading_result.get("service", "")
).buildTransaction({
'gas': 200000,
'gasPrice': w3.toWei('10', 'gwei'),
'nonce': w3.eth.getTransactionCount(self.get_account_address())
})
# Sign and send transaction
signed_txn = w3.eth.account.signTransaction(transaction, self.private_key)
tx_hash = w3.eth.sendRawTransaction(signed_txn.rawTransaction)
# Wait for confirmation
receipt = w3.eth.waitForTransactionReceipt(tx_hash)
return {
"success": True,
"transaction_hash": receipt.transactionHash.hex(),
"certificate_id": certificate_hash,
"block_number": receipt.blockNumber,
"gas_used": receipt.gasUsed
}
except Exception as e:
return {
"success": False,
"error": f"Blockchain submission failed: {str(e)}"
}
def verify_certificate(self, certificate_id: str) -> Dict:
"""Verify authentication certificate on blockchain"""
from web3 import Web3
w3 = Web3(Web3.HTTPProvider(self.blockchain_config["rpc_url"]))
contract = w3.eth.contract(
address=self.contract_address,
abi=self.load_contract_abi()
)
try:
# Query blockchain for certificate
certificate_data = contract.functions.getCertificate(certificate_id).call()
if certificate_data[0]: # Certificate exists
return {
"valid": True,
"card_id": certificate_data[1],
"grade": certificate_data[2],
"service": certificate_data[3],
"timestamp": certificate_data[4],
"block_number": certificate_data[5],
"immutable": True
}
else:
return {"valid": False, "error": "Certificate not found"}
except Exception as e:
return {"valid": False, "error": f"Verification failed: {str(e)}"}
Step 4: Custom Authentication Workflow
In-House Authentication Service
from datetime import datetime, timedelta
import uuid
from enum import Enum
class AuthenticationStatus(Enum):
SUBMITTED = "submitted"
IN_REVIEW = "in_review"
IMAGING = "imaging"
GRADING = "grading"
QUALITY_ASSURANCE = "quality_assurance"
CERTIFIED = "certified"
REJECTED = "rejected"
class InHouseAuthenticationService:
def __init__(self, api_client, db_connection):
self.api_client = api_client
self.db = db_connection
self.setup_authentication_tables()
def setup_authentication_tables(self) -> None:
"""Set up authentication workflow tables"""
tables_sql = """
CREATE TABLE IF NOT EXISTS authentication_submissions (
id VARCHAR(36) PRIMARY KEY,
card_id VARCHAR(50),
submitter_id VARCHAR(50),
submission_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
declared_value DECIMAL(10,2),
estimated_condition VARCHAR(20),
special_instructions TEXT,
status ENUM('submitted','in_review','imaging','grading','qa','certified','rejected'),
assigned_authenticator VARCHAR(50),
priority_level ENUM('standard','expedited','express') DEFAULT 'standard',
tracking_number VARCHAR(20) UNIQUE,
FOREIGN KEY (card_id) REFERENCES cards(id)
);
CREATE TABLE IF NOT EXISTS authentication_results (
id VARCHAR(36) PRIMARY KEY,
submission_id VARCHAR(36),
final_grade DECIMAL(3,1),
condition_grade VARCHAR(20),
authenticity_verified BOOLEAN,
authenticator_notes TEXT,
image_paths JSON,
certificate_number VARCHAR(50) UNIQUE,
authentication_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
quality_score DECIMAL(3,2),
FOREIGN KEY (submission_id) REFERENCES authentication_submissions(id)
);
CREATE TABLE IF NOT EXISTS fraud_alerts (
id VARCHAR(36) PRIMARY KEY,
submission_id VARCHAR(36),
alert_type VARCHAR(50),
risk_score DECIMAL(3,2),
description TEXT,
flagged_by VARCHAR(50),
flagged_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
resolved BOOLEAN DEFAULT FALSE,
resolution_notes TEXT,
FOREIGN KEY (submission_id) REFERENCES authentication_submissions(id)
);
"""
with self.db.cursor() as cursor:
for statement in tables_sql.split(';'):
if statement.strip():
cursor.execute(statement)
self.db.commit()
def submit_for_authentication(self, card_data: Dict, submitter_info: Dict) -> Dict:
"""Submit card for in-house authentication"""
submission_id = str(uuid.uuid4())
tracking_number = self.generate_tracking_number()
# Initial fraud risk assessment
fraud_assessment = FraudDetectionService(self.api_client).assess_fraud_risk(
card_data, submitter_info
)
# Determine processing priority
priority = self.determine_priority(submitter_info, fraud_assessment)
# Insert submission record
submission_query = """
INSERT INTO authentication_submissions
(id, card_id, submitter_id, declared_value, estimated_condition,
special_instructions, status, priority_level, tracking_number)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
"""
with self.db.cursor() as cursor:
cursor.execute(submission_query, (
submission_id,
card_data["id"],
submitter_info["user_id"],
submitter_info.get("declared_value", 0),
submitter_info.get("estimated_condition", "unknown"),
submitter_info.get("special_instructions", ""),
AuthenticationStatus.SUBMITTED.value,
priority,
tracking_number
))
self.db.commit()
# Create fraud alerts if high risk
if fraud_assessment["risk_level"] in ["HIGH", "MEDIUM"]:
self.create_fraud_alert(submission_id, fraud_assessment)
# Record submission in Trading Card API
self.record_api_submission(submission_id, card_data, submitter_info)
return {
"submission_id": submission_id,
"tracking_number": tracking_number,
"estimated_completion": self.calculate_completion_date(priority),
"status": AuthenticationStatus.SUBMITTED.value,
"fraud_risk_level": fraud_assessment["risk_level"],
"processing_fee": self.calculate_fee(submitter_info, priority)
}
def process_authentication_workflow(self, submission_id: str) -> Dict:
"""Process authentication through workflow stages"""
# Get current submission status
current_status = self.get_submission_status(submission_id)
workflow_actions = {
AuthenticationStatus.SUBMITTED: self.initial_review,
AuthenticationStatus.IN_REVIEW: self.detailed_examination,
AuthenticationStatus.IMAGING: self.professional_imaging,
AuthenticationStatus.GRADING: self.condition_grading,
AuthenticationStatus.QUALITY_ASSURANCE: self.final_qa_review
}
if current_status in workflow_actions:
result = workflow_actions[current_status](submission_id)
# Update status if workflow stage completed
if result.get("stage_completed"):
next_status = self.get_next_workflow_status(current_status)
self.update_submission_status(submission_id, next_status)
return result
else:
return {"error": f"Invalid status: {current_status}"}
def initial_review(self, submission_id: str) -> Dict:
"""Perform initial review of submission"""
# Get submission details
submission = self.get_submission_details(submission_id)
# Automated checks
checks = {
"card_exists_in_api": self.verify_card_exists(submission["card_id"]),
"images_uploaded": self.verify_images_uploaded(submission_id),
"metadata_complete": self.verify_metadata_complete(submission),
"payment_verified": self.verify_payment(submission_id),
"no_fraud_alerts": self.check_fraud_alerts(submission_id)
}
# All checks must pass for automatic approval
auto_approve = all(checks.values())
if auto_approve:
self.assign_authenticator(submission_id)
return {
"stage_completed": True,
"next_action": "proceed_to_imaging",
"checks_passed": checks,
"assigned_authenticator": self.get_assigned_authenticator(submission_id)
}
else:
# Flag for manual review
self.flag_for_manual_review(submission_id, checks)
return {
"stage_completed": False,
"requires_manual_review": True,
"failed_checks": {k: v for k, v in checks.items() if not v}
}
def professional_imaging(self, submission_id: str) -> Dict:
"""Professional imaging workflow"""
imaging_protocol = {
"front_image": {"resolution": "600dpi", "lighting": "color_corrected"},
"back_image": {"resolution": "600dpi", "lighting": "color_corrected"},
"edge_images": {"count": 4, "resolution": "300dpi"},
"surface_detail": {"magnification": "10x", "focus_areas": ["corners", "edges", "surface"]},
"uv_imaging": {"enabled": True, "wavelength": "365nm"}
}
# Simulate professional imaging process
imaging_results = {
"images_captured": len(imaging_protocol),
"quality_scores": {
"front": np.random.uniform(0.85, 0.98),
"back": np.random.uniform(0.85, 0.98),
"edges": np.random.uniform(0.80, 0.95),
"surface": np.random.uniform(0.75, 0.95)
},
"uv_anomalies_detected": np.random.choice([True, False], p=[0.1, 0.9]),
"imaging_timestamp": datetime.now().isoformat()
}
# Store imaging results
self.store_imaging_results(submission_id, imaging_results)
return {
"stage_completed": True,
"imaging_quality": "excellent" if min(imaging_results["quality_scores"].values()) > 0.9 else "good",
"anomalies_detected": imaging_results["uv_anomalies_detected"],
"next_action": "proceed_to_grading"
}
def condition_grading(self, submission_id: str) -> Dict:
"""Professional condition grading"""
# Get imaging results
imaging_data = self.get_imaging_results(submission_id)
# Grading criteria
grading_factors = {
"corners": {"weight": 0.25, "score": np.random.uniform(7, 10)},
"edges": {"weight": 0.25, "score": np.random.uniform(7, 10)},
"surface": {"weight": 0.25, "score": np.random.uniform(7, 10)},
"centering": {"weight": 0.25, "score": np.random.uniform(7, 10)}
}
# Calculate composite grade
composite_score = sum(
factor["score"] * factor["weight"]
for factor in grading_factors.values()
)
# Convert to standard grading scale
if composite_score >= 9.5:
grade = 10
condition = "Gem Mint"
elif composite_score >= 9.0:
grade = 9
condition = "Mint"
elif composite_score >= 8.0:
grade = 8
condition = "Near Mint-Mint"
elif composite_score >= 7.0:
grade = 7
condition = "Near Mint"
elif composite_score >= 6.0:
grade = 6
condition = "Excellent-Mint"
else:
grade = max(1, int(composite_score))
condition = "Good" if grade >= 5 else "Poor"
grading_result = {
"final_grade": grade,
"condition": condition,
"composite_score": composite_score,
"individual_scores": grading_factors,
"grading_date": datetime.now().isoformat(),
"grader_id": self.get_assigned_authenticator(submission_id)
}
# Store grading results
self.store_grading_results(submission_id, grading_result)
return {
"stage_completed": True,
"grading_result": grading_result,
"next_action": "quality_assurance"
}
# Certificate generation
class CertificateGenerator:
def __init__(self, template_config: Dict):
self.template_config = template_config
def generate_authentication_certificate(self, submission_id: str,
auth_result: Dict) -> Dict:
"""Generate official authentication certificate"""
from reportlab.pdfgen import canvas
from reportlab.lib.pagesizes import letter
from reportlab.lib.units import inch
from reportlab.lib.colors import HexColor
cert_number = self.generate_certificate_number()
# Create PDF certificate
filename = f"certificate_{cert_number}.pdf"
c = canvas.Canvas(filename, pagesize=letter)
# Certificate header
c.setFont("Helvetica-Bold", 24)
c.drawCentredText(letter[0]/2, letter[1] - inch, "CERTIFICATE OF AUTHENTICITY")
# Certificate number
c.setFont("Helvetica", 12)
c.drawString(inch, letter[1] - 1.5*inch, f"Certificate No: {cert_number}")
c.drawRightString(letter[0] - inch, letter[1] - 1.5*inch,
f"Date: {datetime.now().strftime('%B %d, %Y')}")
# Card details
y_position = letter[1] - 2.5*inch
card_info = auth_result["card_data"]
details = [
("Card Name:", card_info["name"]),
("Year:", str(card_info["year"])),
("Player:", card_info.get("player_name", "N/A")),
("Set:", card_info.get("set_name", "N/A")),
("Number:", card_info.get("number", "N/A")),
("Grade:", f"{auth_result['grade']} ({auth_result['condition']})"),
("Authentication Date:", auth_result["authentication_date"])
]
for label, value in details:
c.setFont("Helvetica-Bold", 11)
c.drawString(inch, y_position, label)
c.setFont("Helvetica", 11)
c.drawString(inch + 120, y_position, value)
y_position -= 20
# Authentication statement
y_position -= 30
c.setFont("Helvetica", 10)
auth_statement = """
This card has been examined and authenticated by our certified professionals.
The grade assigned reflects the card's condition based on industry standards
for corners, edges, surface, and centering. This certificate is backed by
our authenticity guarantee and blockchain verification.
"""
lines = auth_statement.strip().split('\n')
for line in lines:
c.drawString(inch, y_position, line.strip())
y_position -= 15
# QR code for verification
qr_data = {
"cert_number": cert_number,
"card_id": card_info["id"],
"verification_url": f"https://verify.tradingcardapi.com/{cert_number}"
}
# Add signature area
y_position = 2*inch
c.setFont("Helvetica", 10)
c.drawString(inch, y_position, "Authorized by:")
c.line(inch + 80, y_position - 5, inch + 200, y_position - 5)
c.drawString(inch + 80, y_position - 20, "Chief Authenticator")
c.save()
# Store certificate record
certificate_record = {
"certificate_number": cert_number,
"submission_id": submission_id,
"file_path": filename,
"issued_date": datetime.now().isoformat(),
"verification_data": qr_data
}
self.store_certificate_record(certificate_record)
return {
"certificate_number": cert_number,
"certificate_file": filename,
"verification_url": qr_data["verification_url"],
"digital_signature": self.create_digital_signature(cert_number)
}
def create_digital_signature(self, cert_number: str) -> str:
"""Create cryptographic signature for certificate"""
import hashlib
import hmac
# Use HMAC with secret key for signature
secret_key = self.template_config.get("signing_key", "default_secret")
message = f"{cert_number}:{datetime.now().isoformat()}"
signature = hmac.new(
secret_key.encode(),
message.encode(),
hashlib.sha256
).hexdigest()
return signature
Step 5: Quality Assurance and Audit Systems
Comprehensive QA Framework
import logging
from dataclasses import dataclass
from typing import List, Optional
@dataclass
class QualityCheck:
name: str
description: str
severity: str # "critical", "major", "minor"
status: str # "pass", "fail", "warning"
details: Optional[str] = None
class QualityAssuranceService:
def __init__(self, api_client, config: Dict):
self.api_client = api_client
self.config = config
self.logger = self.setup_logging()
def setup_logging(self) -> logging.Logger:
"""Set up audit logging"""
logger = logging.getLogger("authentication_qa")
logger.setLevel(logging.INFO)
# File handler for audit trail
file_handler = logging.FileHandler("authentication_audit.log")
file_handler.setLevel(logging.INFO)
# Console handler
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.WARNING)
# Formatter
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
file_handler.setFormatter(formatter)
console_handler.setFormatter(formatter)
logger.addHandler(file_handler)
logger.addHandler(console_handler)
return logger
def perform_comprehensive_qa(self, submission_id: str) -> Dict:
"""Perform comprehensive quality assurance review"""
self.logger.info(f"Starting QA review for submission {submission_id}")
qa_checks = []
# 1. Authentication accuracy verification
accuracy_check = self.verify_authentication_accuracy(submission_id)
qa_checks.append(accuracy_check)
# 2. Grade consistency verification
grade_check = self.verify_grade_consistency(submission_id)
qa_checks.append(grade_check)
# 3. Image quality verification
image_check = self.verify_image_quality(submission_id)
qa_checks.append(image_check)
# 4. Documentation completeness
docs_check = self.verify_documentation_completeness(submission_id)
qa_checks.append(docs_check)
# 5. Certificate accuracy verification
cert_check = self.verify_certificate_accuracy(submission_id)
qa_checks.append(cert_check)
# 6. Blockchain verification (if applicable)
blockchain_check = self.verify_blockchain_record(submission_id)
qa_checks.append(blockchain_check)
# Analyze QA results
critical_failures = [c for c in qa_checks if c.severity == "critical" and c.status == "fail"]
major_issues = [c for c in qa_checks if c.severity == "major" and c.status == "fail"]
warnings = [c for c in qa_checks if c.status == "warning"]
# Determine overall QA status
if critical_failures:
qa_status = "REJECTED"
recommendation = "Reject authentication - critical issues found"
elif len(major_issues) >= 2:
qa_status = "REQUIRES_REWORK"
recommendation = "Return for correction - multiple major issues"
elif major_issues:
qa_status = "CONDITIONAL_APPROVAL"
recommendation = "Approve with corrections noted"
else:
qa_status = "APPROVED"
recommendation = "Approve for certification"
qa_result = {
"submission_id": submission_id,
"qa_status": qa_status,
"recommendation": recommendation,
"total_checks": len(qa_checks),
"checks_passed": len([c for c in qa_checks if c.status == "pass"]),
"critical_failures": len(critical_failures),
"major_issues": len(major_issues),
"warnings": len(warnings),
"detailed_checks": [c.__dict__ for c in qa_checks],
"qa_timestamp": datetime.now().isoformat(),
"qa_reviewer": self.config.get("qa_reviewer_id", "system")
}
# Log QA completion
self.logger.info(f"QA completed for {submission_id}: {qa_status}")
# Store QA results
self.store_qa_results(submission_id, qa_result)
return qa_result
def verify_authentication_accuracy(self, submission_id: str) -> QualityCheck:
"""Verify authentication accuracy against known standards"""
submission = self.get_submission_details(submission_id)
card_id = submission["card_id"]
# Get card reference data
card_response = self.api_client.get(f"/cards/{card_id}")
card_data = card_response.json()["data"]
# Check critical identification points
checks = {
"year_match": submission["identified_year"] == card_data["attributes"]["year"],
"set_match": submission["identified_set"] == card_data["attributes"]["set_name"],
"number_match": submission["identified_number"] == card_data["attributes"]["number"],
"player_match": submission["identified_player"] == card_data["attributes"]["player_name"]
}
accuracy_score = sum(checks.values()) / len(checks)
if accuracy_score == 1.0:
return QualityCheck(
name="Authentication Accuracy",
description="Card identification verified against API data",
severity="critical",
status="pass",
details=f"All identification points verified (100% accuracy)"
)
elif accuracy_score >= 0.8:
return QualityCheck(
name="Authentication Accuracy",
description="Minor discrepancies in card identification",
severity="major",
status="warning",
details=f"Accuracy: {accuracy_score:.1%}. Failed checks: {[k for k, v in checks.items() if not v]}"
)
else:
return QualityCheck(
name="Authentication Accuracy",
description="Significant identification errors detected",
severity="critical",
status="fail",
details=f"Accuracy: {accuracy_score:.1%}. Multiple identification failures"
)
# Automated audit system
class AuthenticationAuditService:
def __init__(self, db_connection):
self.db = db_connection
def create_audit_trail(self, submission_id: str, action: str,
performer: str, details: Dict) -> None:
"""Create detailed audit trail entry"""
audit_entry = {
"id": str(uuid.uuid4()),
"submission_id": submission_id,
"action_type": action,
"performed_by": performer,
"timestamp": datetime.now().isoformat(),
"details": json.dumps(details),
"ip_address": details.get("ip_address"),
"user_agent": details.get("user_agent")
}
audit_query = """
INSERT INTO authentication_audit_log
(id, submission_id, action_type, performed_by, timestamp,
details, ip_address, user_agent)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
"""
with self.db.cursor() as cursor:
cursor.execute(audit_query, (
audit_entry["id"],
audit_entry["submission_id"],
audit_entry["action_type"],
audit_entry["performed_by"],
audit_entry["timestamp"],
audit_entry["details"],
audit_entry["ip_address"],
audit_entry["user_agent"]
))
self.db.commit()
def generate_audit_report(self, date_range: Tuple[str, str]) -> Dict:
"""Generate comprehensive audit report"""
audit_query = """
SELECT
a.submission_id,
a.action_type,
a.performed_by,
a.timestamp,
s.status,
ar.final_grade,
ar.authenticity_verified
FROM authentication_audit_log a
LEFT JOIN authentication_submissions s ON a.submission_id = s.id
LEFT JOIN authentication_results ar ON s.id = ar.submission_id
WHERE a.timestamp BETWEEN %s AND %s
ORDER BY a.timestamp
"""
audit_data = pd.read_sql(audit_query, self.db, params=date_range)
# Generate summary statistics
summary = {
"total_actions": len(audit_data),
"unique_submissions": audit_data["submission_id"].nunique(),
"action_breakdown": audit_data["action_type"].value_counts().to_dict(),
"performer_breakdown": audit_data["performed_by"].value_counts().to_dict(),
"completion_rate": len(audit_data[audit_data["status"] == "certified"]) / len(audit_data),
"average_grade": audit_data["final_grade"].mean() if "final_grade" in audit_data else None,
"authenticity_rate": audit_data["authenticity_verified"].mean() if "authenticity_verified" in audit_data else None
}
return {
"audit_summary": summary,
"detailed_log": audit_data.to_dict("records"),
"report_generated": datetime.now().isoformat()
}
Step 6: Marketplace Verification Integration
Seller Protection System
class MarketplaceVerificationService {
constructor(apiClient, config) {
this.apiClient = apiClient;
this.config = config;
}
async verifyListingAuthenticity(listingData) {
const verificationResult = {
listingId: listingData.id,
verificationLevel: 'pending',
riskScore: 0,
warnings: [],
recommendations: []
};
// 1. Check if card exists in authenticated database
const cardExists = await this.verifyCardExists(listingData.cardId);
if (!cardExists.found) {
verificationResult.warnings.push('Card not found in authenticated database');
verificationResult.riskScore += 0.3;
}
// 2. Verify seller authentication history
const sellerHistory = await this.checkSellerHistory(listingData.sellerId);
if (sellerHistory.fraudAlerts > 0) {
verificationResult.warnings.push(`Seller has ${sellerHistory.fraudAlerts} fraud alerts`);
verificationResult.riskScore += 0.4;
}
// 3. Image authenticity verification
const imageVerification = await this.verifyListingImages(listingData.images);
verificationResult.riskScore += imageVerification.riskScore;
verificationResult.warnings.push(...imageVerification.warnings);
// 4. Price consistency check
const priceCheck = await this.verifyPriceConsistency(
listingData.cardId,
listingData.price,
listingData.condition
);
if (priceCheck.isPriceAnomalous) {
verificationResult.warnings.push(priceCheck.warning);
verificationResult.riskScore += 0.2;
}
// 5. Certificate verification
if (listingData.certificateNumber) {
const certVerification = await this.verifyCertificate(listingData.certificateNumber);
if (!certVerification.valid) {
verificationResult.warnings.push('Certificate verification failed');
verificationResult.riskScore += 0.5;
}
}
// Determine verification level
if (verificationResult.riskScore >= 0.8) {
verificationResult.verificationLevel = 'high_risk';
verificationResult.recommendations.push('Block listing pending manual review');
} else if (verificationResult.riskScore >= 0.5) {
verificationResult.verificationLevel = 'medium_risk';
verificationResult.recommendations.push('Flag for enhanced buyer protection');
} else if (verificationResult.riskScore >= 0.3) {
verificationResult.verificationLevel = 'low_risk';
verificationResult.recommendations.push('Standard marketplace protections apply');
} else {
verificationResult.verificationLevel = 'verified';
verificationResult.recommendations.push('Listing verified as authentic');
}
// Store verification result
await this.storeVerificationResult(verificationResult);
return verificationResult;
}
async verifyListingImages(images) {
const imageAnalysis = {
riskScore: 0,
warnings: []
};
for (const image of images) {
// Check for digital manipulation
const manipulationCheck = await this.detectImageManipulation(image.url);
if (manipulationCheck.manipulated) {
imageAnalysis.warnings.push(`Image ${image.id} shows signs of digital manipulation`);
imageAnalysis.riskScore += 0.3;
}
// Check image metadata for inconsistencies
const metadataCheck = await this.analyzeImageMetadata(image.url);
if (metadataCheck.suspicious) {
imageAnalysis.warnings.push(`Image ${image.id} has suspicious metadata`);
imageAnalysis.riskScore += 0.2;
}
// Reverse image search
const reverseSearch = await this.performReverseImageSearch(image.url);
if (reverseSearch.foundElsewhere) {
imageAnalysis.warnings.push(`Image ${image.id} found on other listings`);
imageAnalysis.riskScore += 0.4;
}
}
return imageAnalysis;
}
async checkSellerHistory(sellerId) {
const response = await this.apiClient.get(`/users/${sellerId}/authentication-history`);
if (response.status === 200) {
const history = response.data;
return {
totalSubmissions: history.totalSubmissions || 0,
successfulAuthentications: history.successfulAuthentications || 0,
fraudAlerts: history.fraudAlerts || 0,
averageGrade: history.averageGrade || 0,
reputationScore: history.reputationScore || 0.5
};
}
return {
totalSubmissions: 0,
successfulAuthentications: 0,
fraudAlerts: 0,
averageGrade: 0,
reputationScore: 0.5
};
}
async verifyCertificate(certificateNumber) {
// Check multiple grading services
const verificationSources = [
() => this.verifyPSACertificate(certificateNumber),
() => this.verifyBGSCertificate(certificateNumber),
() => this.verifySGCCertificate(certificateNumber),
() => this.verifyInHouseCertificate(certificateNumber)
];
for (const verifyFunction of verificationSources) {
try {
const result = await verifyFunction();
if (result.valid) {
return {
valid: true,
service: result.service,
grade: result.grade,
certificationDate: result.certificationDate,
verificationSource: result.source
};
}
} catch (error) {
console.warn(`Certificate verification failed for ${certificateNumber}:`, error);
}
}
return {
valid: false,
error: 'Certificate not found in any verification database'
};
}
}
Step 7: Insurance and Appraisal Integration
Insurance Valuation Service
class InsuranceAppraisalService:
def __init__(self, api_client, insurance_config: Dict):
self.api_client = api_client
self.insurance_config = insurance_config
def generate_insurance_appraisal(self, card_ids: List[str],
appraisal_type: str = "replacement") -> Dict:
"""Generate comprehensive insurance appraisal"""
appraisal_id = str(uuid.uuid4())
appraisal_data = {
"appraisal_id": appraisal_id,
"appraisal_type": appraisal_type,
"appraisal_date": datetime.now().isoformat(),
"appraiser_credentials": self.insurance_config["appraiser_info"],
"total_items": len(card_ids),
"individual_valuations": [],
"summary_statistics": {}
}
total_value = 0
for card_id in card_ids:
# Get card authentication data
auth_response = self.api_client.get(f"/cards/{card_id}/authentication")
if auth_response.status_code == 200:
auth_data = auth_response.json()["data"]
# Calculate insurance value
insurance_value = self.calculate_insurance_value(
card_id, auth_data, appraisal_type
)
appraisal_data["individual_valuations"].append({
"card_id": card_id,
"card_name": auth_data["card_name"],
"grade": auth_data.get("grade"),
"condition": auth_data.get("condition"),
"certificate_number": auth_data.get("certificate_number"),
"market_value": insurance_value["market_value"],
"insurance_value": insurance_value["insurance_value"],
"valuation_method": insurance_value["method"],
"comparable_sales": insurance_value["comparables"]
})
total_value += insurance_value["insurance_value"]
# Calculate summary statistics
values = [v["insurance_value"] for v in appraisal_data["individual_valuations"]]
appraisal_data["summary_statistics"] = {
"total_collection_value": total_value,
"average_card_value": np.mean(values),
"median_card_value": np.median(values),
"highest_value_card": max(values) if values else 0,
"value_distribution": {
"under_100": len([v for v in values if v < 100]),
"100_to_500": len([v for v in values if 100 <= v < 500]),
"500_to_1000": len([v for v in values if 500 <= v < 1000]),
"over_1000": len([v for v in values if v >= 1000])
}
}
# Generate formal appraisal document
appraisal_document = self.generate_appraisal_document(appraisal_data)
return {
"appraisal_id": appraisal_id,
"total_value": total_value,
"document_path": appraisal_document["file_path"],
"validity_period": 365, # Days
"appraisal_summary": appraisal_data["summary_statistics"]
}
def calculate_insurance_value(self, card_id: str, auth_data: Dict,
appraisal_type: str) -> Dict:
"""Calculate insurance value based on authentication and market data"""
# Get recent market sales
market_response = self.api_client.get(f"/cards/{card_id}/market-data", params={
"condition": auth_data.get("condition"),
"grade": auth_data.get("grade"),
"days_back": 90
})
market_data = market_response.json()["data"] if market_response.status_code == 200 else {}
# Base market value
recent_sales = market_data.get("recent_sales", [])
if recent_sales:
market_value = np.median([sale["price"] for sale in recent_sales])
else:
# Fallback to estimated value
market_value = market_data.get("estimated_value", 0)
# Apply insurance multipliers based on appraisal type
multipliers = {
"replacement": 1.25, # 25% premium for replacement cost
"actual_cash_value": 1.0, # Current market value
"agreed_value": 1.15 # 15% premium for agreed value policies
}
multiplier = multipliers.get(appraisal_type, 1.0)
insurance_value = market_value * multiplier
# Apply authentication premium
if auth_data.get("grade"):
grade = float(auth_data["grade"])
if grade >= 9:
insurance_value *= 1.5 # 50% premium for high grades
elif grade >= 8:
insurance_value *= 1.3 # 30% premium for good grades
return {
"market_value": market_value,
"insurance_value": insurance_value,
"method": f"{appraisal_type}_with_authentication_premium",
"multiplier_applied": multiplier,
"grade_premium": auth_data.get("grade", 0),
"comparables": recent_sales[:5] # Top 5 comparable sales
}
Step 8: API Integration Patterns
Authentication Service API Endpoints
const express = require('express');
const multer = require('multer');
const app = express();
// Authentication service REST API
class AuthenticationAPI {
constructor(authService, config) {
this.authService = authService;
this.config = config;
this.setupRoutes();
}
setupRoutes() {
const upload = multer({ dest: 'uploads/' });
// Submit card for authentication
app.post('/api/authenticate/submit', upload.array('images', 10), async (req, res) => {
try {
const submissionData = {
cardId: req.body.cardId,
declaredValue: parseFloat(req.body.declaredValue),
estimatedCondition: req.body.estimatedCondition,
specialInstructions: req.body.specialInstructions,
submitterId: req.user.id,
images: req.files.map(file => ({
filename: file.filename,
originalname: file.originalname,
path: file.path,
size: file.size
}))
};
const result = await this.authService.submitForAuthentication(submissionData);
res.status(201).json({
success: true,
submissionId: result.submissionId,
trackingNumber: result.trackingNumber,
estimatedCompletion: result.estimatedCompletion,
processingFee: result.processingFee
});
} catch (error) {
res.status(400).json({
success: false,
error: error.message
});
}
});
// Check authentication status
app.get('/api/authenticate/status/:trackingNumber', async (req, res) => {
try {
const status = await this.authService.getAuthenticationStatus(
req.params.trackingNumber
);
res.json({
trackingNumber: req.params.trackingNumber,
status: status.currentStatus,
progress: status.progressPercentage,
estimatedCompletion: status.estimatedCompletion,
lastUpdate: status.lastUpdate,
statusHistory: status.history
});
} catch (error) {
res.status(404).json({
error: 'Authentication record not found'
});
}
});
// Get authentication certificate
app.get('/api/authenticate/certificate/:certificateNumber', async (req, res) => {
try {
const certificate = await this.authService.getCertificate(
req.params.certificateNumber
);
if (certificate.exists) {
res.json({
valid: true,
certificateData: {
cardId: certificate.cardId,
grade: certificate.grade,
condition: certificate.condition,
authenticationDate: certificate.authenticationDate,
authenticator: certificate.authenticator,
blockchainHash: certificate.blockchainHash
}
});
} else {
res.status(404).json({
valid: false,
error: 'Certificate not found'
});
}
} catch (error) {
res.status(500).json({
error: 'Certificate verification failed'
});
}
});
// Bulk authentication for collections
app.post('/api/authenticate/bulk-submit', upload.array('images', 100), async (req, res) => {
try {
const bulkData = JSON.parse(req.body.bulkData);
const images = req.files;
const results = await this.authService.submitBulkAuthentication({
items: bulkData.items,
images: images,
submitterId: req.user.id,
serviceLevel: bulkData.serviceLevel || 'standard'
});
res.status(201).json({
success: true,
batchId: results.batchId,
totalItems: results.totalItems,
estimatedCompletion: results.estimatedCompletion,
totalCost: results.totalCost,
individualSubmissions: results.submissions
});
} catch (error) {
res.status(400).json({
success: false,
error: error.message
});
}
});
// Authentication analytics
app.get('/api/authenticate/analytics', async (req, res) => {
try {
const analytics = await this.authService.getAuthenticationAnalytics({
dateRange: {
start: req.query.startDate,
end: req.query.endDate
},
aggregateBy: req.query.aggregateBy || 'daily'
});
res.json(analytics);
} catch (error) {
res.status(500).json({
error: 'Failed to generate analytics'
});
}
});
}
// Webhook for external grading services
setupWebhooks() {
app.post('/api/webhooks/psa-update', async (req, res) => {
try {
const psaUpdate = req.body;
// Verify webhook signature
const isValidSignature = this.verifyWebhookSignature(
req.headers['x-psa-signature'],
req.body
);
if (!isValidSignature) {
return res.status(401).json({ error: 'Invalid signature' });
}
// Process PSA status update
await this.authService.processPSAUpdate(psaUpdate);
res.status(200).json({ received: true });
} catch (error) {
res.status(500).json({ error: 'Webhook processing failed' });
}
});
app.post('/api/webhooks/bgs-update', async (req, res) => {
try {
const bgsUpdate = req.body;
// Process BGS status update
await this.authService.processBGSUpdate(bgsUpdate);
res.status(200).json({ received: true });
} catch (error) {
res.status(500).json({ error: 'Webhook processing failed' });
}
});
}
}
Step 9: Business Workflow Management
Fee Calculation and Service Management
from decimal import Decimal
from datetime import datetime, timedelta
class AuthenticationBusinessService:
def __init__(self, api_client, pricing_config: Dict):
self.api_client = api_client
self.pricing_config = pricing_config
def calculate_authentication_fee(self, submission_data: Dict) -> Dict:
"""Calculate authentication fee based on service level and card value"""
base_fees = self.pricing_config["base_fees"]
declared_value = Decimal(str(submission_data.get("declared_value", 0)))
service_level = submission_data.get("service_level", "standard")
# Base service fee
base_fee = Decimal(str(base_fees[service_level]))
# Value-based fee (percentage of declared value)
value_multipliers = {
"standard": Decimal("0.02"), # 2%
"expedited": Decimal("0.03"), # 3%
"express": Decimal("0.05") # 5%
}
value_fee = declared_value * value_multipliers[service_level]
# Additional service fees
additional_fees = Decimal("0")
if submission_data.get("blockchain_certificate"):
additional_fees += Decimal("25") # Blockchain recording fee
if submission_data.get("insurance_appraisal"):
additional_fees += Decimal("50") # Insurance documentation
if submission_data.get("rush_processing"):
additional_fees += base_fee * Decimal("0.5") # 50% rush surcharge
# Bulk discount
item_count = submission_data.get("item_count", 1)
if item_count >= 10:
bulk_discount = (base_fee + value_fee) * Decimal("0.15") # 15% discount
elif item_count >= 5:
bulk_discount = (base_fee + value_fee) * Decimal("0.10") # 10% discount
else:
bulk_discount = Decimal("0")
# Calculate total
subtotal = base_fee + value_fee + additional_fees - bulk_discount
tax_rate = Decimal(str(self.pricing_config.get("tax_rate", 0.08)))
tax = subtotal * tax_rate
total_fee = subtotal + tax
return {
"fee_breakdown": {
"base_fee": float(base_fee),
"value_fee": float(value_fee),
"additional_fees": float(additional_fees),
"bulk_discount": float(bulk_discount),
"subtotal": float(subtotal),
"tax": float(tax),
"total": float(total_fee)
},
"service_level": service_level,
"declared_value": float(declared_value),
"estimated_completion": self.calculate_completion_date(service_level),
"payment_terms": self.pricing_config["payment_terms"]
}
def manage_authentication_queue(self) -> Dict:
"""Manage authentication processing queue"""
# Get current queue status
queue_query = """
SELECT
priority_level,
status,
COUNT(*) as count,
AVG(DATEDIFF(NOW(), submission_date)) as avg_age_days
FROM authentication_submissions
WHERE status IN ('submitted', 'in_review', 'imaging', 'grading', 'qa')
GROUP BY priority_level, status
ORDER BY
FIELD(priority_level, 'express', 'expedited', 'standard'),
FIELD(status, 'submitted', 'in_review', 'imaging', 'grading', 'qa')
"""
queue_data = pd.read_sql(queue_query, self.db)
# Calculate capacity and throughput
capacity_metrics = self.calculate_capacity_metrics()
# Identify bottlenecks
bottlenecks = self.identify_workflow_bottlenecks(queue_data)
# Generate queue management recommendations
recommendations = self.generate_queue_recommendations(queue_data, capacity_metrics)
return {
"queue_summary": queue_data.to_dict("records"),
"capacity_metrics": capacity_metrics,
"bottlenecks": bottlenecks,
"recommendations": recommendations,
"queue_snapshot_time": datetime.now().isoformat()
}
def calculate_capacity_metrics(self) -> Dict:
"""Calculate processing capacity and throughput metrics"""
# Get historical throughput data
throughput_query = """
SELECT
DATE(authentication_date) as date,
COUNT(*) as completed_authentications,
AVG(DATEDIFF(authentication_date, submission_date)) as avg_processing_days
FROM authentication_results ar
JOIN authentication_submissions s ON ar.submission_id = s.id
WHERE authentication_date >= DATE_SUB(CURDATE(), INTERVAL 30 DAY)
GROUP BY DATE(authentication_date)
ORDER BY date
"""
throughput_data = pd.read_sql(throughput_query, self.db)
# Calculate capacity metrics
if not throughput_data.empty:
daily_capacity = throughput_data["completed_authentications"].mean()
avg_processing_time = throughput_data["avg_processing_days"].mean()
else:
daily_capacity = 0
avg_processing_time = 0
# Get current staffing levels
staffing_query = """
SELECT
role,
COUNT(*) as count,
AVG(efficiency_rating) as avg_efficiency
FROM authentication_staff
WHERE active = TRUE
GROUP BY role
"""
staffing_data = pd.read_sql(staffing_query, self.db)
return {
"daily_capacity": daily_capacity,
"avg_processing_time_days": avg_processing_time,
"current_staffing": staffing_data.to_dict("records"),
"utilization_rate": self.calculate_utilization_rate(),
"capacity_forecast": self.forecast_capacity_needs()
}
# Customer communication system
class AuthenticationCommunicationService:
def __init__(self, email_config: Dict, sms_config: Dict):
self.email_config = email_config
self.sms_config = sms_config
def send_status_update(self, submission_id: str, status_change: Dict) -> None:
"""Send automated status updates to customers"""
# Get customer contact info
customer_info = self.get_customer_info(submission_id)
# Prepare notification content
message_templates = {
"submitted": "Your authentication submission has been received.",
"in_review": "Your card is now under review by our authentication team.",
"imaging": "Professional imaging of your card is in progress.",
"grading": "Your card is being graded by our certified professionals.",
"qa": "Final quality assurance review is underway.",
"certified": "Authentication complete! Your certificate is ready.",
"rejected": "Unfortunately, we cannot authenticate this item."
}
status = status_change["new_status"]
message = message_templates.get(status, "Status updated")
# Send email notification
if customer_info.get("email"):
self.send_email_notification(
customer_info["email"],
f"Authentication Update - {status_change['tracking_number']}",
message,
status_change
)
# Send SMS for high-priority updates
if customer_info.get("phone") and status in ["certified", "rejected"]:
self.send_sms_notification(
customer_info["phone"],
f"Authentication {status}: {status_change['tracking_number']}. "
f"Check email for details."
)
def generate_completion_report(self, submission_id: str) -> Dict:
"""Generate comprehensive completion report for customer"""
# Get all submission data
submission = self.get_submission_details(submission_id)
auth_result = self.get_authentication_result(submission_id)
qa_data = self.get_qa_results(submission_id)
report = {
"submission_summary": {
"tracking_number": submission["tracking_number"],
"submission_date": submission["submission_date"],
"completion_date": auth_result["authentication_date"],
"processing_time_days": (
datetime.fromisoformat(auth_result["authentication_date"]) -
datetime.fromisoformat(submission["submission_date"])
).days
},
"authentication_results": {
"final_grade": auth_result["final_grade"],
"condition": auth_result["condition"],
"authenticity_verified": auth_result["authenticity_verified"],
"certificate_number": auth_result["certificate_number"],
"quality_score": auth_result["quality_score"]
},
"quality_assurance": {
"qa_status": qa_data["qa_status"],
"checks_performed": qa_data["total_checks"],
"checks_passed": qa_data["checks_passed"],
"qa_reviewer": qa_data["qa_reviewer"]
},
"market_impact": self.calculate_market_impact(
submission["card_id"],
auth_result["final_grade"]
),
"next_steps": self.generate_next_steps_recommendations(auth_result)
}
return report
Step 10: Testing and Quality Assurance
Comprehensive Testing Framework
import pytest
import unittest.mock as mock
from unittest.mock import MagicMock, patch
class TestAuthenticationService:
@pytest.fixture
def auth_service(self):
"""Create authentication service instance for testing"""
mock_api_client = MagicMock()
mock_db = MagicMock()
return InHouseAuthenticationService(mock_api_client, mock_db)
@pytest.fixture
def sample_card_data(self):
"""Sample card data for testing"""
return {
"id": "test_card_123",
"attributes": {
"name": "Test Player Rookie Card",
"year": 2020,
"player_name": "Test Player",
"set_name": "Test Set",
"number": "1",
"image_url": "https://example.com/card.jpg"
}
}
def test_card_identification_success(self, auth_service, sample_card_data):
"""Test successful card identification"""
identifier = CardIdentificationService(auth_service.api_client)
# Mock API response
auth_service.api_client.get.return_value.json.return_value = {
"data": [sample_card_data]
}
with patch.object(identifier, 'calculate_match_score', return_value=0.95):
result = identifier.identify_card_from_image("test_image.jpg")
assert result["identification_success"] is True
assert result["top_match"]["match_score"] == 0.95
assert result["top_match"]["card_data"]["id"] == "test_card_123"
def test_fraud_detection_high_risk(self, auth_service):
"""Test fraud detection for high-risk submission"""
fraud_service = FraudDetectionService(auth_service.api_client)
high_risk_data = {
"declared_value": 10000, # Unusually high value
"image_quality_score": 0.2, # Poor image quality
"user_history_score": 0.1 # Poor user history
}
with patch.object(fraud_service, 'detect_statistical_anomalies', return_value=0.9):
risk_assessment = fraud_service.assess_fraud_risk({}, high_risk_data)
assert risk_assessment["risk_level"] == "HIGH"
assert risk_assessment["recommendation"] == "REJECT"
def test_grading_consistency(self, auth_service):
"""Test grading consistency across multiple graders"""
test_submissions = [
{"submission_id": f"test_{i}", "expected_grade": 8.5}
for i in range(10)
]
grades = []
for submission in test_submissions:
result = auth_service.condition_grading(submission["submission_id"])
grades.append(result["grading_result"]["final_grade"])
# Check grade consistency (standard deviation should be low)
grade_std = np.std(grades)
assert grade_std < 1.0, f"Grade inconsistency too high: {grade_std}"
def test_certificate_generation(self, auth_service):
"""Test certificate generation and verification"""
cert_generator = CertificateGenerator({"signing_key": "test_key"})
auth_result = {
"card_data": {
"id": "test_card_123",
"name": "Test Card",
"year": 2020
},
"grade": 9,
"condition": "Mint",
"authentication_date": datetime.now().isoformat()
}
certificate = cert_generator.generate_authentication_certificate(
"test_submission", auth_result
)
assert "certificate_number" in certificate
assert "certificate_file" in certificate
assert "verification_url" in certificate
assert certificate["certificate_file"].endswith(".pdf")
def test_api_integration(self, auth_service):
"""Test Trading Card API integration"""
# Test card lookup
auth_service.api_client.get.return_value.status_code = 200
auth_service.api_client.get.return_value.json.return_value = {
"data": {"id": "test_card", "attributes": {"name": "Test Card"}}
}
result = auth_service.api_client.get("/cards/test_card")
assert result.status_code == 200
assert result.json()["data"]["id"] == "test_card"
@pytest.mark.integration
def test_end_to_end_workflow(self, auth_service, sample_card_data):
"""Test complete authentication workflow"""
# Submit for authentication
submission_data = {
"card_id": sample_card_data["id"],
"declared_value": 100,
"estimated_condition": "near_mint",
"user_id": "test_user"
}
submission_result = auth_service.submit_for_authentication(
sample_card_data, submission_data
)
assert "submission_id" in submission_result
assert "tracking_number" in submission_result
submission_id = submission_result["submission_id"]
# Process through workflow stages
workflow_stages = [
AuthenticationStatus.SUBMITTED,
AuthenticationStatus.IN_REVIEW,
AuthenticationStatus.IMAGING,
AuthenticationStatus.GRADING,
AuthenticationStatus.QUALITY_ASSURANCE
]
for stage in workflow_stages:
with patch.object(auth_service, 'get_submission_status', return_value=stage):
result = auth_service.process_authentication_workflow(submission_id)
assert result.get("stage_completed") is not False
# Performance testing
class AuthenticationPerformanceTests:
def test_bulk_submission_performance(self):
"""Test performance with bulk submissions"""
import time
auth_service = InHouseAuthenticationService(mock_api_client, mock_db)
# Simulate 100 card bulk submission
start_time = time.time()
bulk_data = {
"items": [{"card_id": f"card_{i}", "declared_value": 50} for i in range(100)],
"submitter_id": "test_user",
"service_level": "standard"
}
with patch.object(auth_service, 'submit_bulk_authentication') as mock_submit:
mock_submit.return_value = {"batch_id": "test_batch", "total_items": 100}
result = auth_service.submit_bulk_authentication(bulk_data)
processing_time = time.time() - start_time
# Should process 100 items in under 5 seconds
assert processing_time < 5.0
assert result["total_items"] == 100
def test_concurrent_authentication_processing(self):
"""Test concurrent processing of multiple authentications"""
import concurrent.futures
import threading
auth_service = InHouseAuthenticationService(mock_api_client, mock_db)
def process_single_authentication(submission_id):
return auth_service.process_authentication_workflow(submission_id)
submission_ids = [f"test_submission_{i}" for i in range(20)]
start_time = time.time()
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
futures = [
executor.submit(process_single_authentication, sub_id)
for sub_id in submission_ids
]
results = [future.result() for future in concurrent.futures.as_completed(futures)]
processing_time = time.time() - start_time
# Should handle 20 concurrent processes efficiently
assert processing_time < 10.0
assert len(results) == 20
# Run tests
if __name__ == "__main__":
pytest.main([__file__, "-v"])
Security and Compliance
Data Protection and Privacy
from cryptography.fernet import Fernet
import bcrypt
from datetime import datetime, timedelta
class AuthenticationSecurityService:
def __init__(self, security_config: Dict):
self.encryption_key = security_config["encryption_key"].encode()
self.fernet = Fernet(self.encryption_key)
self.security_config = security_config
def encrypt_sensitive_data(self, data: str) -> str:
"""Encrypt sensitive authentication data"""
return self.fernet.encrypt(data.encode()).decode()
def decrypt_sensitive_data(self, encrypted_data: str) -> str:
"""Decrypt sensitive authentication data"""
return self.fernet.decrypt(encrypted_data.encode()).decode()
def hash_password(self, password: str) -> str:
"""Hash user passwords securely"""
salt = bcrypt.gensalt()
return bcrypt.hashpw(password.encode('utf-8'), salt).decode('utf-8')
def verify_password(self, password: str, hashed: str) -> bool:
"""Verify password against hash"""
return bcrypt.checkpw(password.encode('utf-8'), hashed.encode('utf-8'))
def implement_data_retention_policy(self) -> Dict:
"""Implement data retention and cleanup policies"""
# Authentication records retention: 7 years
# Personal data retention: Per user consent or legal requirements
# Audit logs retention: 10 years
retention_policies = {
"authentication_submissions": {"years": 7, "table": "authentication_submissions"},
"authentication_results": {"years": 7, "table": "authentication_results"},
"audit_logs": {"years": 10, "table": "authentication_audit_log"},
"customer_data": {"years": 5, "table": "customer_information", "consent_required": True}
}
cleanup_results = {}
for policy_name, policy in retention_policies.items():
cutoff_date = datetime.now() - timedelta(days=policy["years"] * 365)
cleanup_query = f"""
DELETE FROM {policy["table"]}
WHERE created_at < %s
"""
# Add consent check for customer data
if policy.get("consent_required"):
cleanup_query += " AND (data_retention_consent = FALSE OR data_retention_consent IS NULL)"
with self.db.cursor() as cursor:
cursor.execute(cleanup_query, (cutoff_date,))
rows_deleted = cursor.rowcount
cleanup_results[policy_name] = {
"rows_deleted": rows_deleted,
"cutoff_date": cutoff_date.isoformat()
}
self.db.commit()
return {
"cleanup_completed": datetime.now().isoformat(),
"policies_applied": cleanup_results,
"next_cleanup_scheduled": (datetime.now() + timedelta(days=30)).isoformat()
}
def audit_access_patterns(self) -> Dict:
"""Audit access patterns for security monitoring"""
# Detect unusual access patterns
access_query = """
SELECT
performed_by,
action_type,
DATE(timestamp) as access_date,
COUNT(*) as action_count,
COUNT(DISTINCT submission_id) as unique_submissions
FROM authentication_audit_log
WHERE timestamp >= DATE_SUB(NOW(), INTERVAL 30 DAY)
GROUP BY performed_by, action_type, DATE(timestamp)
ORDER BY access_date DESC, action_count DESC
"""
access_data = pd.read_sql(access_query, self.db)
# Identify anomalies
anomalies = []
# Check for excessive access
daily_limits = {
"image_download": 1000,
"grade_assignment": 100,
"certificate_generation": 50
}
for _, row in access_data.iterrows():
action_type = row["action_type"]
count = row["action_count"]
if action_type in daily_limits and count > daily_limits[action_type]:
anomalies.append({
"type": "excessive_access",
"user": row["performed_by"],
"action": action_type,
"count": count,
"limit": daily_limits[action_type],
"date": row["access_date"]
})
return {
"access_summary": access_data.to_dict("records"),
"anomalies_detected": anomalies,
"monitoring_period": "30 days",
"audit_timestamp": datetime.now().isoformat()
}
# Compliance and regulatory features
class ComplianceManager:
def __init__(self, compliance_config: Dict):
self.compliance_config = compliance_config
def generate_compliance_report(self, report_type: str, period: str) -> Dict:
"""Generate compliance reports for regulatory requirements"""
if report_type == "authentication_activity":
return self.generate_authentication_activity_report(period)
elif report_type == "financial_transactions":
return self.generate_financial_transactions_report(period)
elif report_type == "data_protection":
return self.generate_data_protection_report(period)
else:
raise ValueError(f"Unknown report type: {report_type}")
def ensure_gdpr_compliance(self, user_id: str, request_type: str) -> Dict:
"""Handle GDPR compliance requests"""
if request_type == "data_export":
return self.export_user_data(user_id)
elif request_type == "data_deletion":
return self.delete_user_data(user_id)
elif request_type == "consent_withdrawal":
return self.handle_consent_withdrawal(user_id)
else:
raise ValueError(f"Unknown GDPR request type: {request_type}")
# Usage example
auth_service = InHouseAuthenticationService(api_client, db_connection)
fraud_detector = FraudDetectionService(api_client)
qa_service = QualityAssuranceService(api_client, qa_config)
# Process authentication workflow
submission_result = auth_service.submit_for_authentication(card_data, submission_data)
print(f"Submission created: {submission_result['tracking_number']}")
# Check fraud risk
fraud_assessment = fraud_detector.assess_fraud_risk(card_data, submission_data)
print(f"Fraud risk level: {fraud_assessment['risk_level']}")
# Perform quality assurance
qa_result = qa_service.perform_comprehensive_qa(submission_result['submission_id'])
print(f"QA status: {qa_result['qa_status']}")
Best Practices Summary
Authentication Service Excellence
- Implement multi-stage verification workflows
- Use multiple detection methods for fraud prevention
- Maintain comprehensive audit trails
- Provide transparent customer communication
- Ensure consistent grading standards
Security and Trust
- Encrypt all sensitive data in transit and at rest
- Implement blockchain verification for immutable records
- Use digital signatures for certificate authenticity
- Monitor access patterns for security anomalies
- Maintain compliance with applicable regulations
Quality Assurance
- Establish rigorous QA processes at each workflow stage
- Use statistical analysis to ensure grading consistency
- Implement automated checks with manual review escalation
- Maintain comprehensive documentation for all processes
- Regular calibration and training for authentication staff
Business Operations
- Implement transparent pricing and fee structures
- Provide real-time status tracking for customers
- Optimize workflow capacity and throughput
- Maintain integration with major grading services
- Establish clear liability and guarantee policies
This comprehensive guide provides the foundation for building trusted authentication services that protect collectors, maintain market integrity, and establish industry-leading standards for card verification and grading.