[paper]Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
本文提出了一个防御算法，在不改变深度神经网络的结构并且在尽可能小的影响模型准确率的前提下能够有效地抵御对抗样本的攻击。 We use the knowledge extracted during distillation to reduce the amplitude of network gradients exploited by adversaries to craft adversaria
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相关文章
- 1. 关于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. 【转】A Beginner's Guide To Understanding Convolutional Neural Networks