# MindArmour Release Notes ## MindArmour 2.0.0 Release Notes ### API Change * Add version check with MindSpore. ### Contributors Thanks goes to these wonderful people: Liu Zhidan, Zhang Shukun, Liu Liu, Tang Cong. Contributions of any kind are welcome! ## MindArmour 1.9.0 Release Notes ### API Change * Add Chinese version api of natural robustness feature. ### Contributors Thanks goes to these wonderful people: Liu Zhidan, Zhang Shukun, Jin Xiulang, Liu Liu, Tang Cong, Yangyuan. Contributions of any kind are welcome! ## MindArmour 1.8.0 Release Notes ### API Change * Add Chinese version of all existed api. ### Contributors Thanks goes to these wonderful people: Zhang Shukun, Liu Zhidan, Jin Xiulang, Liu Liu, Tang Cong, Yangyuan. Contributions of any kind are welcome! ## MindArmour 1.7.0 Release Notes ### Major Features and Improvements #### Robustness * [STABLE] Real-World Robustness Evaluation Methods ### API Change * Change value of parameter `mutate_config` in `mindarmour.fuzz_testing.Fuzzer.fuzzing` interface. ([!333](https://gitee.com/mindspore/mindarmour/pulls/333)) ### Bug fixes * Update version of third-party dependence pillow from more than or equal to 6.2.0 to more than or equal to 7.2.0. ([!329](https://gitee.com/mindspore/mindarmour/pulls/329)) ### Contributors Thanks goes to these wonderful people: Liu Zhidan, Zhang Shukun, Jin Xiulang, Liu Liu. Contributions of any kind are welcome! # MindArmour 1.６.0 ## MindArmour 1.６.0 Release Notes ### Major Features and Improvements #### Reliability * [BETA] Data Drift Detection for Image Data * [BETA] Model Fault Injection ### Bug fixes ### Contributors Thanks goes to these wonderful people: Wu Xiaoyu，Feng Zhenye, Liu Zhidan, Jin Xiulang, Liu Luobin, Liu Liu, Zhang Shukun # MindArmour 1.5.0 ## MindArmour 1.5.0 Release Notes ### Major Features and Improvements #### Reliability * [BETA] Reconstruct AI Fuzz and Neuron Coverage Metrics ### Bug fixes ### Contributors Thanks goes to these wonderful people: Wu Xiaoyu，Liu Zhidan, Jin Xiulang, Liu Luobin, Liu Liu # MindArmour 1.3.0-rc1 ## MindArmour 1.3.0 Release Notes ### Major Features and Improvements #### Privacy * [STABLE] Data Drift Detection for Time Series Data ### Bug fixes * [BUGFIX] Optimization of API description. ### Contributors Thanks goes to these wonderful people: Wu Xiaoyu，Liu Zhidan, Jin Xiulang, Liu Luobin, Liu Liu # MindArmour 1.2.0 ## MindArmour 1.2.0 Release Notes ### Major Features and Improvements #### Privacy * [STABLE] Tailored-based privacy protection technology (Pynative) * [STABLE] Model Inversion. Reverse analysis technology of privacy information ### API Change #### Backwards Incompatible Change ##### C++ API [Modify] ... [Add] ... [Delete] ... ##### Java API [Add] ... #### Deprecations ##### C++ API ##### Java API ### Bug fixes [BUGFIX] ... ### Contributors Thanks goes to these wonderful people: han.yin # MindArmour 1.1.0 Release Notes ## MindArmour ### Major Features and Improvements * [STABLE] Attack capability of the Object Detection models. * Some white-box adversarial attacks, such as [iterative] gradient method and DeepFool now can be applied to Object Detection models. * Some black-box adversarial attacks, such as PSO and Genetic Attack now can be applied to Object Detection models. ### Backwards Incompatible Change #### Python API #### C++ API ### Deprecations #### Python API #### C++ API ### New Features #### Python API #### C++ API ### Improvements #### Python API #### C++ API ### Bug fixes #### Python API #### C++ API ## Contributors Thanks goes to these wonderful people: Xiulang Jin, Zhidan Liu, Luobin Liu and Liu Liu. Contributions of any kind are welcome! # Release 1.0.0 ## Major Features and Improvements ### Differential privacy model training * Privacy leakage evaluation. * Parameter verification enhancement. * Support parallel computing. ### Model robustness evaluation * Fuzzing based Adversarial Robustness testing. * Parameter verification enhancement. ### Other * Api & Directory Structure * Adjusted the directory structure based on different features. * Optimize the structure of examples. ## Bugfixes ## Contributors Thanks goes to these wonderful people: Liu Liu, Xiulang Jin, Zhidan Liu and Luobin Liu. Contributions of any kind are welcome! # Release 0.7.0-beta ## Major Features and Improvements ### Differential privacy model training * Privacy leakage evaluation. * Using Membership inference to evaluate the effectiveness of privacy-preserving techniques for AI. ### Model robustness evaluation * Fuzzing based Adversarial Robustness testing. * Coverage-guided test set generation. ## Bugfixes ## Contributors Thanks goes to these wonderful people: Liu Liu, Xiulang Jin, Zhidan Liu, Luobin Liu and Huanhuan Zheng. Contributions of any kind are welcome! # Release 0.6.0-beta ## Major Features and Improvements ### Differential privacy model training * Optimizers with differential privacy * Differential privacy model training now supports some new policies. * Adaptive Norm policy is supported. * Adaptive Noise policy with exponential decrease is supported. * Differential Privacy Training Monitor * A new monitor is supported using zCDP as its asymptotic budget estimator. ## Bugfixes ## Contributors Thanks goes to these wonderful people: Liu Liu, Huanhuan Zheng, XiuLang jin, Zhidan liu. Contributions of any kind are welcome. # Release 0.5.0-beta ## Major Features and Improvements ### Differential privacy model training * Optimizers with differential privacy * Differential privacy model training now supports both Pynative mode and graph mode. * Graph mode is recommended for its performance. ## Bugfixes ## Contributors Thanks goes to these wonderful people: Liu Liu, Huanhuan Zheng, Xiulang Jin, Zhidan Liu. Contributions of any kind are welcome! # Release 0.3.0-alpha ## Major Features and Improvements ### Differential Privacy Model Training Differential Privacy is coming! By using Differential-Privacy-Optimizers, one can still train a model as usual, while the trained model preserved the privacy of training dataset, satisfying the definition of differential privacy with proper budget. * Optimizers with Differential Privacy([PR23](https://gitee.com/mindspore/mindarmour/pulls/23), [PR24](https://gitee.com/mindspore/mindarmour/pulls/24)) * Some common optimizers now have a differential privacy version (SGD/Adam). We are adding more. * Automatically and adaptively add Gaussian Noise during training to achieve Differential Privacy. * Automatically stop training when Differential Privacy Budget exceeds. * Differential Privacy Monitor([PR22](https://gitee.com/mindspore/mindarmour/pulls/22)) * Calculate overall budget consumed during training, indicating the ultimate protect effect. ## Bug fixes ## Contributors Thanks goes to these wonderful people: Liu Liu, Huanhuan Zheng, Zhidan Liu, Xiulang Jin Contributions of any kind are welcome! # Release 0.2.0-alpha ## Major Features and Improvements * Add a white-box attack method: M-DI2-FGSM([PR14](https://gitee.com/mindspore/mindarmour/pulls/14)). * Add three neuron coverage metrics: KMNCov, NBCov, SNACov([PR12](https://gitee.com/mindspore/mindarmour/pulls/12)). * Add a coverage-guided fuzzing test framework for deep neural networks([PR13](https://gitee.com/mindspore/mindarmour/pulls/13)). * Update the MNIST Lenet5 examples. * Remove some duplicate code. ## Bug fixes ## Contributors Thanks goes to these wonderful people: Liu Liu, Huanhuan Zheng, Zhidan Liu, Xiulang Jin Contributions of any kind are welcome! # Release 0.1.0-alpha Initial release of MindArmour. ## Major Features * Support adversarial attack and defense on the platform of MindSpore. * Include 13 white-box and 7 black-box attack methods. * Provide 5 detection algorithms to detect attacking in multiple way. * Provide adversarial training to enhance model security. * Provide 6 evaluation metrics for attack methods and 9 evaluation metrics for defense methods.