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MindSpore Armour Release Notes

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MindSpore Armour 2.0.0 Release Notes

Major Features and Improvements

  • Add version check with MindSpore.
  • Upgrade the related software packages, Pillow>=9.3.0，scipy>=1.5.2，pytest>=5.4.3.

Contributors

Thanks goes to these wonderful people:

Zhang Shukun, Liu Zhidan, Jin Xiulang, Liu Liu, Tang Cong, Yang Yuan, Li Hongcheng.

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)

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)

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, PR24)

    • 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)

    • 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).
  • Add three neuron coverage metrics: KMNCov, NBCov, SNACov(PR12).
  • Add a coverage-guided fuzzing test framework for deep neural networks(PR13).
  • 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.