[paper]Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
本文提出了一個防禦算法，在不改變深度神經網絡的結構並且在儘可能小的影響模型準確率的前提下能夠有效地抵禦對抗樣本的攻擊。 We use the knowledge extracted during distillation to reduce the amplitude of network gradients exploited by adversaries to craft adversaria
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相關文章
- 1. 關於Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks的理解
- 2. A New Defense Against Adversarial Images: Turning a Weakness into a Strength
- 3. Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks
- 4. Adversarial Perturbations
- 5. Use a load-balancer as a first row of defense against DDOS
- 6. [paper]Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
- 7. Improving Deep Neural Networks
- 8. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 9. 【Bias 02】BENCHMARKING NEURAL NETWORK ROBUSTNESS TO COMMON CORRUPTIONS AND PERTURBATIONS
- 10. 【轉】A Beginner's Guide To Understanding Convolutional Neural Networks