advml-workshop

advml-workshop's Stars

• thu-ml/MMTrustEval

  A toolbox for benchmarking trustworthiness of multimodal large language models (MultiTrust, NeurIPS 2024 Track Datasets and Benchmarks)

  Language:Python1228

• dongyp13/Non-Targeted-Adversarial-Attacks

  A non-targeted adversarial attack method, which won the first place in NIPS 2017 non-targeted adversarial attacks competition

  Language:Python24454

• thu-ml/Attack-Bard

  Language:Python916

• thu-ml/AT3D

  Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition, CVPR 2023, Highlight

  Language:Python404

• thu-ml/ares

  A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.

  Language:Python49488

• thu-ml/3D_Corruptions_AD

  Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving, CVPR 2023

  Language:Python12216

• banyuanhao/PAP

  Language:Python231

• P2333/Mixup-Inference

  Further improve robustness of mixup-trained models in inference (ICLR 2020)

  Language:Python609

• P2333/Adaptive-Diversity-Promoting

  Adversarial Defense for Ensemble Models (ICML 2019)

  Language:Python6112

• P2333/Reverse-Cross-Entropy

  Reverse Cross Entropy for Adversarial Detection (NeurIPS 2018)

  Language:Python4510

• P2333/Max-Mahalanobis-Training

  Max Mahalanobis Training (ICML 2018 + ICLR 2020)

  Language:Python9020

• P2333/Bag-of-Tricks-for-AT

  Empirical tricks for training robust models (ICLR 2021)

  Language:Python25226

• P2333/Rectified-Rejection

  Coupling rejection strategy against adversarial attacks (CVPR 2022)

  Language:Python285