(CAV).Therefore,a reliable RL system is the foundation for the security critical applications in Al,which has attracted a concern that is more critical than ever.However,recent studies discover that the interesting attack mode adversarialattack also be effective when targeting neural ne...
Attack on Reinforcement Learning: Tactics of Adversarial Attack on Deep Reinforcement Learning Agents(IJCAI 2017):提出针对深度强化学习的攻击方式。作者单位:National Tsing Hua University, NVIDIA. 防御:如何让机器学习模型更鲁棒 防御的方法基本可以分为三类: 修改训练过程或者数据输入(Modified training/input) 修...
Reinforcement learning is a core technology for modern artificial intelligence, and it has become a workhorse for AI applications ranging from Atrai Game to Connected and Automated Vehicle System (CAV). Therefore, a reliable RL system is the foundation f
2.2. Adversarial Attack and Defense Attack:为分类生成对抗的例子最近得到了广泛的研究。[Intriguing properties of neural networks.]首先展示了对抗的例子,通过在原始图像中加入视觉上难以察觉的扰动来计算,使得CNNs能够非常准确地预测出一个错误的标签。[FGSM]提出了一种基于CNNs线性特性的简单快速梯度符号生成对抗算...
Adversarial examples for other tasks willbe investigated in Section V.Inspired by [102], we def ine the Threat Model in this paperas follows:• The adversaries can attack only at the testing/deployingarXiv:1712.07107v1 [cs.LG] 19 Dec 2017 ...
Tsai etal. [124]提出了一种在物理世界中的3D打印攻击方法,但在电子场景下生成的对抗样本在真实世界很容易受到各种干扰。Wen et al[134]Geometry-Aware Adversarial Attack重组了3D点云以生成对抗3D打印。 攻击方法 分白盒和黑盒攻击,白盒攻击更容易,但黑盒攻击更具有实用价值。
In this study, we assume a graybox attack, where the adversary is aware of the architecture and parameters of the classifier; however, the attacker has limited knowledge about the defense mechanism (if any). Intentionally limiting the attacker’s knowledge makes the threat more complicated, ...
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples Deep learning algorithms and networks are vulnerable to perturbed inputs which is known as the adversarial attack. Many defense methodologies have been investigated to defend against such adversarial attack. In this work, we prop...
Decoupling Direction and Norm for Efficient Gradient-Based Adversarial Attacks and Defenses 说在前面 1.提出的问题 2.提出的方法 2.1 相关工作 2.2 算法介绍 3.实验结果 3.1 Untargeted Attack 3.2 Targeted Attack 3.3 Defense Evaluation 4.结论 Decoupling Direction and Norm for Efficient Gradient-Based L2 Ad...
然后,作者在总结之前的工作时提到了两种基于梯度的攻击方法(但不是针对于图神经网络的),分别为:FastGradientSignMethod(FGSM)attack和Jacobian−basedSaliencyMapApproach(JSMA)attack。 FGSM: η=ϵsign(∇Jθ(x,l)),其中ϵ表示扰动的大小,sign获取梯度的符号。