/Adversarial-Learning-Papers

Adversarial learning papers

Papers

ICLR 2021 high score papers

  1. Geometry-aware Instance-reweighted Adversarial Training
  2. Improving Adversarial Robustness via Channel-wise Activation Suppressing
  3. How Does Mixup Help With Robustness and Generalization?
  4. Towards A Unified Understanding and Improving of Adversarial Transferability
  5. When Optimizing $f$-Divergence is Robust with Label Noise
  6. A Hypergradient Approach to Robust Regression without Correspondence Does enhanced shape bias improve neural network robustness to common corruptions?
  7. you only need adversarial supervision for semantic image synthesis
  8. SOAR: Second-Order Adversarial Regularization
  9. ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity
  10. Perceptual Adversarial Robustness: Generalizable Defenses Against Unforeseen Threat Models
  11. Collective Robustness Certificates
  12. Perceptual Adversarial Robustness: Generalizable Defenses Against Unforeseen Threat Models
  13. Distributionally Robust Learning for Unsupervised Domain Adaptation
  14. On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
  15. Evaluations and Methods for Explanation through Robustness Analysis
  16. Fast Geometric Projections for Local Robustness Certification
  17. Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds

Attack

White Box attack

  1. Stronger and Faster Wasserstein Adversarial Attacks ~pdf

Poisoning

  1. Transferable Clean-Label Poisoning Attacks on Deep Neural Nets ~pdf

Backdoor attack

  1. Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning~pdf

Attack in other domains

  1. DBA: Distributed Backdoor Attacks Against Federated Learning~pdf
  2. Adversarial Attacks on Copyright Detection Systems ~pdf

Defense

Adversarial learning variants

  1. Theoretically Principled Trade-off between Robustness and Accuracy~pdf
  2. You only propagate once: Accelerating adversarial training via maximal principle~pdf
  3. Fast is better than free: Revisiting adversarial training~pdf
  4. Attacks Which Do Not Kill Training Make Adversarial Learning Stronger~pdf
  5. Towards Understanding the Dynamics of the First-Order Adversaries~pdf

Provalable defense

  1. Certified Adversarial Robustness via Randomized Smoothing~pdf

Others

  1. Adverserial Examples are Not Bugs, They are Features. ~pdf ~Intro
  2. Rethinking the security of skip connections in resent-like neural networks. ~pdf ~Intro