對抗樣本(論文解讀十一):PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning
PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning Chenglin Yang, Adam Kortylewski, Cihang Xie, Yinzhi Cao, and Alan Yuille Johns Hopkins University 通過強化學習實現一個基於紋理的黑盒攻擊 這是一篇比較新的
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- 1. 對抗樣本(論文解讀一): DPATCH: An Adversarial Patch Attack on Object Detectors
- 2. 對抗樣本(論文解讀五):Perceptual-Sensitive GAN for Generating Adversarial Patches
- 3. 對抗樣本(論文解讀八):Towards More Robust Adversarial Attack Against Real World Object Detectors
- 4. 對抗樣本(論文解讀七):On Physical Adversarial Patches for Object Detection
- 5. 對抗樣本(論文解讀六):Adversarial camera stickers: A physical camera-based attack on deep learning systems
- 6. 對抗樣本(論文解讀四): Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
- 7. COMA(一)： Learning to Communicate with Deep Multi-Agent Reinforcement Learning 論文講解
- 8. 對抗樣本(論文解讀三): Adversarial Examples Improve Image Recognition
- 9. RL論文閱讀【四】Deep Reinforcement Learning with Double Q-learning（Double DQN）
- 10. Adversary Attack(對抗攻擊)論文閱讀筆記
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相關文章
- 1. 對抗樣本(論文解讀一): DPATCH: An Adversarial Patch Attack on Object Detectors
- 2. 對抗樣本(論文解讀五):Perceptual-Sensitive GAN for Generating Adversarial Patches
- 3. 對抗樣本(論文解讀八):Towards More Robust Adversarial Attack Against Real World Object Detectors
- 4. 對抗樣本(論文解讀七):On Physical Adversarial Patches for Object Detection
- 5. 對抗樣本(論文解讀六):Adversarial camera stickers: A physical camera-based attack on deep learning systems
- 6. 對抗樣本(論文解讀四): Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
- 7. COMA(一)： Learning to Communicate with Deep Multi-Agent Reinforcement Learning 論文講解
- 8. 對抗樣本(論文解讀三): Adversarial Examples Improve Image Recognition
- 9. RL論文閱讀【四】Deep Reinforcement Learning with Double Q-learning（Double DQN）
- 10. Adversary Attack(對抗攻擊)論文閱讀筆記