MindArmour Documents

AI is the catalyst for change but also faces challengs in security and privacy protection. MindArmour provides adversarial robustness, model security tests, differential privacy training, privacy risk assessment, and data drift detection.

Typical Application Scenarios

1. Adversarial Example

   Includes capabilities such as white and black box adversarial attacks, adversarial training, and adversarial example detection, to help personnel generate adversarial examples and evaluate the robustness of AI models.

2. Privacy Risk Assessment

   Uses algorithms such as membership inference attack and model inversion attack to assess the privacy risk for models.

3. Privacy Protection

   Emhances model privacy and protects user data using differential training and protection suppression mechanisms.

4. Reliability

   Detects data distribution changes in time and predicts the symptoms of model failure in advance, which is of great significance for the timely adjustment of the AI model through multiple data drift detection algorithms.

5. Fuzz

   Provides a coverage-guided fuzzing tool that features flexible, customizable test policies and metrics, and uses neuron coverage to guide input mutation so that the input can activate neurons and distribute neuron values in a wider range. In this way, we can discover different types of model output results and incorrect behaviors.

6. Model Encryption

   Uses the symmetric encryption algorithm to encrypt the parameter files or inference models to protect the model files. Directly loads the ciphertext model to implement inference or incremental training when using the algorithm.

Installation

• MindArmour Installation

AI Security

• Improving Model Security with NAD Algorithm
• Testing Model Security Using Fuzz Testing
• Using Membership Inference to Test Model Security

AI Privacy

• Protecting User Privacy with Differential Privacy Mechanism
• Protecting User Privacy with Suppress Privacy
• Model Encryption Protection

AI Reliability

• Implementing the Concept Drift Detection Application of Time Series Data
• Implementing the Concept Drift Detection Application of Image Data
• Implementing the Model Fault Injection and Evaluation

API References

• mindarmour
• mindarmour.adv_robustness.attacks
• mindarmour.adv_robustness.defenses
• mindarmour.adv_robustness.detectors
• mindarmour.adv_robustness.evaluations
• mindarmour.fuzz_testing
• mindarmour.natural_robustness.transform.image
• mindarmour.privacy.diff_privacy
• mindarmour.privacy.evaluation
• mindarmour.privacy.sup_privacy
• mindarmour.reliability
• mindarmour.utils

References

• Overall Security and Trustworthiness Design
• Differential Privacy Design
• AI Model Security Testing Design
• MindArmour Module Introduction
• FAQ

RELEASE NOTES

• Release Notes

Click to download the API documentation