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Mitigation Strategies Analysis for davidsandberg/facenet

Mitigation Strategy: Encryption at Rest for Facial Embeddings (Facenet Output)

1. Encryption at Rest for Facial Embeddings (Facenet Output)

  • Mitigation Strategy: Encryption at Rest for Facial Embeddings
  • Description:
    1. Choose Encryption Algorithm: Select a strong encryption algorithm like AES-256 to encrypt the storage location of facial embeddings generated by facenet.
    2. Secure Key Management: Implement a robust key management system. For production, consider using a Hardware Security Module (HSM) or dedicated key management service. For development, utilize secure key storage mechanisms provided by your operating system or cloud provider.
    3. Encryption Implementation: Integrate encryption into the application's data storage layer specifically for facial embeddings. This might involve:
      • Database encryption features if embeddings are stored in a database.
      • Encrypting the file system or directories where embedding files are saved.
      • Using encryption libraries within the application code to encrypt/decrypt embeddings before writing/reading from storage. Ensure this is applied specifically to the facenet embedding data.
    4. Restrict Key Access: Limit access to encryption keys to only authorized application components and personnel that require access to decrypt facial embeddings.
    5. Regular Key Rotation: Implement a policy for regular key rotation to enhance security and limit the impact of potential key compromise over time.
  • List of Threats Mitigated:
    • Data Breach of Stored Facenet Embeddings (High Severity): If the storage medium containing facial embeddings (output of facenet) is compromised, unencrypted embeddings could be exposed. These embeddings are sensitive biometric data derived directly from facenet's processing and their exposure can lead to privacy violations and potential misuse for identity theft or surveillance.
  • Impact:
    • Data Breach of Stored Facenet Embeddings: Significantly Reduces Risk. Encryption renders the stored facenet embeddings unreadable to unauthorized parties even if they gain access to the storage, protecting the sensitive biometric data generated by facenet.
  • Currently Implemented:
    • To be determined. Check if the application currently encrypts the storage location where facial embeddings generated by facenet are saved. Specify where encryption is implemented (e.g., database level, file system level, application level, and specifically for facenet embeddings). If not implemented, state "Not Implemented".
  • Missing Implementation:
    • If "Not Implemented" above, encryption at rest for facenet facial embeddings is missing. Specify the storage locations where facenet embeddings are saved that are currently not encrypted and require encryption implementation.

Mitigation Strategy: Liveness Detection to Protect Facenet System from Spoofing

2. Liveness Detection to Protect Facenet System from Spoofing

  • Mitigation Strategy: Liveness Detection Implementation
  • Description:
    1. Select Liveness Detection Method: Choose a liveness detection technique to verify that the input to the facenet system is from a live person and not a spoof. Options include:
      • Passive Liveness: Analyze image characteristics (texture, reflection, etc.) of the input image before feeding it to facenet, looking for signs of spoofing.
      • Active Liveness: Implement challenge-response mechanisms requiring user interaction (blinking, smiling, head movements) before or during the facial recognition process using facenet.
      • Depth-Based Liveness: If hardware allows, utilize depth sensors for 3D facial analysis before or in conjunction with facenet processing.
    2. Integrate with Facenet Workflow: Integrate the chosen liveness detection method into the facial recognition workflow before the image is processed by facenet for embedding generation or comparison. This ensures that only potentially live faces are processed by facenet.
    3. Threshold Configuration: Configure appropriate thresholds for liveness detection to balance security and user experience. Adjust thresholds to minimize false positives (rejecting live users) and false negatives (accepting spoofs) in the context of your facenet application.
    4. User Feedback: Provide clear feedback to the user about the liveness detection process and results, especially if liveness detection fails and prevents facenet processing.
    5. Regular Testing and Improvement: Regularly test the effectiveness of the liveness detection implementation against various spoofing attempts to ensure it effectively protects the facenet system. Refine the method and thresholds as needed based on testing and evolving spoofing techniques.
  • List of Threats Mitigated:
    • Spoofing Attacks Bypassing Facenet Recognition (High Severity): Without liveness detection, attackers can easily bypass the facial recognition system built with facenet using photos, videos, masks, or other presentation attack instruments. This allows unauthorized access or actions by deceiving the facenet based system.
  • Impact:
    • Spoofing Attacks Bypassing Facenet Recognition: Significantly Reduces Risk. Liveness detection makes it much harder for attackers to use spoofing techniques to impersonate legitimate users and deceive the facenet system. The level of risk reduction depends on the sophistication and effectiveness of the chosen liveness detection method used in conjunction with facenet.
  • Currently Implemented:
    • To be determined. Check if liveness detection is currently implemented in the application's facial recognition process before or in conjunction with facenet processing. Specify the type of liveness detection method used (if any) and how it integrates with the facenet workflow. If not implemented, state "Not Implemented".
  • Missing Implementation:
    • If "Not Implemented" above, liveness detection is missing. Specify where liveness detection needs to be integrated into the facial recognition workflow before facenet processing to mitigate spoofing attacks against the facenet based system.