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Intro
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Classroom Contents
Evaluating Neural Network Robustness - Targeted Attacks and Defenses
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- 1 Intro
- 2 Summary: Terminology (cont.) Targeted Attack Metrics
- 3 Existing Attacks
- 4 Fast Gradient Sign (FGS)
- 5 Jacobian-based Saliency Map Attack (SMA)
- 6 New approach
- 7 Objective Functions Explored
- 8 Dealing with Box Constraints: x+8 € [0, 1]
- 9 Finding Best Combination
- 10 Different Attacks (Cont.)
- 11 Attack Evaluation
- 12 Attacks on ImageNet
- 13 Defensive Distillation
