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. .. raw:: html <img src="https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r1.8/docs/mindarmour/docs/source_en/images/mindarmour.png" width="700px" alt="" > Typical Application Scenarios ----------------------------- 1. `Adversarial Example <https://www.mindspore.cn/mindarmour/docs/en/r1.8/improve_model_security_nad.html>`_ 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 <https://www.mindspore.cn/mindarmour/docs/en/r1.8/test_model_security_membership_inference.html>`_ Uses algorithms such as membership inference attack and model inversion attack to assess the privacy risk for models. 3. `Privacy Protection <https://www.mindspore.cn/mindarmour/docs/en/r1.8/protect_user_privacy_with_differential_privacy.html>`_ Emhances model privacy and protects user data using differential training and protection suppression mechanisms. 4. `Reliability <https://www.mindspore.cn/mindarmour/docs/en/r1.8/concept_drift_time_series.html>`_ 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 <https://www.mindspore.cn/mindarmour/docs/en/r1.8/test_model_security_fuzzing.html>`_ 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 <https://www.mindspore.cn/mindarmour/docs/en/r1.8/model_encrypt_protection.html>`_ 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. .. toctree:: :glob: :maxdepth: 1 :caption: Installation mindarmour_install .. toctree:: :glob: :maxdepth: 1 :caption: AI Security improve_model_security_nad test_model_security_fuzzing test_model_security_membership_inference .. toctree:: :glob: :maxdepth: 1 :caption: AI Privacy protect_user_privacy_with_differential_privacy protect_user_privacy_with_suppress_privacy model_encrypt_protection .. toctree:: :glob: :maxdepth: 1 :caption: AI Reliability concept_drift_time_series concept_drift_images fault_injection .. toctree:: :maxdepth: 1 :caption: 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 .. toctree:: :glob: :maxdepth: 1 :caption: References design differential_privacy_design fuzzer_design security_and_privacy faq .. toctree:: :glob: :maxdepth: 1 :caption: RELEASE NOTES RELEASE