Random Projections for improved Adversarial Robustness
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Updated
Apr 11, 2020 - Jupyter Notebook
Random Projections for improved Adversarial Robustness
Lipschitz Neural Networks described in "Sorting Out Lipschitz Function Approximation" (ICML 2019).
[Partial] RADLER: (adversarially) Robust Adversarial Distributional LEaRner
Square Attack: a query-efficient black-box adversarial attack via random search [ECCV 2020]
Imbalanced Gradients: A New Cause of Overestimated Adversarial Robustness. (MD attacks)
📕 Adversarial Attacks and Defenses for Image-Based Recommendation Systems using Deep Neural Networks.
Contact: Alexander Hartl, Maximilian Bachl, Fares Meghdouri. Explainability methods and Adversarial Robustness metrics for RNNs for Intrusion Detection Systems. Also contains code for "SparseIDS: Learning Packet Sampling with Reinforcement Learning" (branch "rl").
Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs
Connecting Interpretability and Robustness in Decision Trees through Separation
[NeurIPS2020] The official repository of "Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks".
LAFEAT: Piercing Through Adversarial Defenses with Latent Features (CVPR 2021 Oral)
Contains notebooks for the PAR tutorial at CVPR 2021.
[TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training
[ICLR 2020] ”Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference“
[ICML 2021 Long Talk] "Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm" by Mingkang Zhu, Tianlong Chen, Zhangyang Wang
[ICLR 2021] "Robust Overfitting may be mitigated by properly learned smoothening" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chang, Zhangyang Wang
[CVPR 2020] Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
[ECCV 2020 AROW Workshop] A Deep Dive into Adversarial Robustness in Zero-Shot Learning
[NeurIPS'20 Oral] DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
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