Threat: Data Poisoning (High Severity)
- Threat: Data Poisoning (Training Data Manipulation)
- Description: An attacker injects malicious or mislabeled face images into the training dataset if the application allows retraining or fine-tuning of the Facenet model. This can be done by compromising data sources, intercepting data pipelines, or exploiting vulnerabilities in data upload mechanisms. The attacker aims to degrade model accuracy, introduce bias, or create backdoors for specific faces, allowing them to bypass recognition or cause misidentification.
- Impact: Reduced accuracy of facial recognition, potential for unauthorized access, system malfunction due to biased predictions, reputational damage, and legal repercussions due to inaccurate or discriminatory outcomes.
- Facenet Component Affected: Training process, potentially the pre-trained model if fine-tuning overwrites it.
- Risk Severity: High
- Mitigation Strategies:
- Implement strict input validation and sanitization for all training data.
- Establish secure data pipelines with integrity checks and provenance tracking.
- Utilize anomaly detection to identify and remove potentially poisoned data.
- Restrict access to training environments and data to authorized personnel.
- Regularly audit training data and model performance for anomalies.
- Threat: Model Inversion and Extraction
- Description: An attacker attempts to reverse-engineer the deployed Facenet model to extract sensitive information or the model itself. This could involve querying the model extensively through public APIs, exploiting vulnerabilities in API endpoints, or using specialized model inversion techniques. The attacker aims to understand the model's architecture, parameters, or biases, potentially to create adversarial attacks, bypass security measures, or reuse the model for malicious purposes.
- Impact: Exposure of proprietary model knowledge, potential for development of targeted adversarial attacks, circumvention of security features relying on the model, reputational damage if model details are leaked, and potential intellectual property theft.
- Facenet Component Affected: Deployed Facenet model (specifically the model weights and architecture).
- Risk Severity: High
- Mitigation Strategies:
- Implement robust API security measures, including authentication and authorization.
- Apply rate limiting to API endpoints interacting with the Facenet model.
- Consider model obfuscation techniques (with limited effectiveness for deep learning).
- Monitor API usage for suspicious patterns indicative of model extraction attempts.
- Deploy model behind a secure gateway and restrict direct access.
- Threat: Adversarial Attacks (Evasion and Spoofing) - Presentation Attacks (Spoofing)
- Description: An attacker uses presentation attacks (spoofing) using photos, videos, masks, or deepfakes to impersonate someone else. The attacker presents these fake faces to the facial recognition system to gain unauthorized access or manipulate the system. Spoofing techniques can range from simple printed photos to sophisticated 3D masks or digitally generated deepfakes.
- Impact: Unauthorized access, identity theft, circumvention of security controls, potential financial loss or data breaches if access is granted to sensitive resources, and reputational damage.
- Facenet Component Affected: Face recognition module, specifically the face detection and verification functions.
- Risk Severity: High
- Mitigation Strategies:
- Implement robust liveness detection mechanisms (blink detection, motion detection, texture analysis, depth sensing).
- Utilize multi-factor authentication, combining facial recognition with other methods.
- Regularly update liveness detection techniques to counter evolving spoofing methods.
- Train users to be aware of spoofing risks and report suspicious activity.
- Consider using hardware-based liveness detection for higher security applications.
- Threat: Privacy and Data Security Risks - Unauthorized Access, Storage, or Disclosure
- Description: An attacker gains unauthorized access to sensitive facial recognition data (face images, embeddings) stored or processed by the application. This could be through exploiting vulnerabilities in data storage systems, network breaches, insider threats, or weak access controls. The attacker aims to steal biometric data for identity theft, surveillance, or other malicious purposes, violating user privacy and potentially breaching regulations.
- Impact: Privacy violations, identity theft, regulatory non-compliance, legal repercussions, reputational damage, and loss of user trust.
- Facenet Component Affected: Data storage and handling components, including databases, file systems, and API endpoints that manage facial data.
- Risk Severity: Critical
- Mitigation Strategies:
- Minimize data collection and storage; store only necessary data for the shortest duration.
- Anonymize or pseudonymize facial data; store embeddings instead of raw images if possible.
- Implement strong encryption for data at rest and in transit.
- Enforce strict access control policies (least privilege principle).
- Establish and enforce clear data retention and deletion policies.
- Ensure compliance with relevant privacy regulations (GDPR, CCPA, etc.).
- Regularly audit access to facial data and security controls.
- Threat: Privacy and Data Security Risks - Misuse of Facial Recognition Data
- Description: Facial recognition data collected for a specific, legitimate purpose is misused for other unintended or unauthorized purposes. This could be done by internal actors with access to the data or through data breaches leading to external misuse. Misuse can include unauthorized surveillance, profiling, or sharing data with third parties without consent.
- Impact: Privacy violations, ethical concerns, reputational damage, legal repercussions, loss of user trust, and potential for discriminatory practices.
- Facenet Component Affected: Application logic and data handling processes that govern how facial data is used after initial recognition.
- Risk Severity: High
- Mitigation Strategies:
- Clearly define and document the purpose for data collection and usage.
- Implement technical and organizational controls to prevent data misuse.
- Provide users with transparency and control over their data usage.
- Regularly audit data usage to ensure compliance with defined purposes and policies.
- Implement data minimization principles to limit the scope of potential misuse.
- Establish clear ethical guidelines for facial recognition data usage.
- Threat: Bias and Fairness Issues
- Description: The Facenet model exhibits bias against certain demographic groups due to biased training data, leading to unfair or discriminatory outcomes. While not a direct security vulnerability, it can be exploited to cause harm or unfair treatment to specific groups. This bias can manifest as lower accuracy or higher false positive/negative rates for certain demographics.
- Impact: Unfair or discriminatory outcomes, reputational damage, legal and ethical concerns, loss of user trust, and potential for social harm.
- Facenet Component Affected: Facenet model itself (inherent bias from training data).
- Risk Severity: High (in sensitive contexts like law enforcement, access control, etc.)
- Mitigation Strategies:
- Evaluate the model for bias using fairness metrics and diverse datasets.
- Use diverse and representative training data if retraining or fine-tuning.
- Implement bias detection and mitigation techniques.
- Regularly audit system fairness and accuracy across demographics.
- Be transparent about potential limitations and biases to users.
- Consider using fairness-aware machine learning techniques.