- DaST: Data-free Substitute Training for Adversarial Attacks
- The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
- Robustness Guarantees for Deep Neural Networks on Videos
- A Self-supervised Approach for Adversarial Robustness
- Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations
- Unpaired Image Super-Resolution using Pseudo-Supervision
- How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework
- Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization
- Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs
- Benchmarking Adversarial Robustness on Image Classification
- What it Thinks is Important is Important: Robustness Transfers through Input Gradients
- Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identificationWith Deep Mis-Ranking
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"It is strange that only extraordinary men make the discoveries, which later appear so easy and simple."― Georg C. Lichtenberg
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