单次攻击(One-time attacks)只需要优化一次对抗样本。 迭代攻击(Iterative attacks)需要多次更新对抗样本。 与单次攻击相比,迭代攻击通常会得到更好的对抗样本,但需要与受害分类器进行更多交互(更多查询),并且需要更多的计算时间来生成。对于一些计算密集型任务(例如,强化学习),单次攻击可能是唯一可行的选择。 3.2 扰动...
There are also several adversarial attacks for discrete data that apply to other distance metrics, such as the number of dropped points [15] and the semantic similarity [16]. 2.3. Threat models There are three mainstream threat models for adversarial attacks and defenses: the black-box, gray-...
Audio Adversarial Examples: Targeted Attacks on Speech-to-Texthttps://arxiv.org/pdf/1801.01944.pdf Adversarial T-shirt! Evading Person Detectors in A Physical Worldhttps://arxiv.org/pdf/1910.11099.pdf Adversarial Attacks and Defenses in Deep Learninghttps://doi.org/10.1016/j.eng.2019.12.012 “...
5.对抗防御 通常包括对抗训练、基于随机的方案、去噪方法、可证明防御以及一些其他方法。 5.1对抗训练 对抗训练:通过与对抗样本一起训练,来尝试提高神经网络的鲁棒性。 通常情况下,可视为如下定义的最大最小游戏: 其中, 代表对抗代价,θ代表网络权重,x‘代表对抗输入,y代表真相标签。D(x,x')代表x和x'之间的距离...
Previous studies have shown lingering doubts about medical DNNs and their vulnerability to adversarial attacks. Although various defense methods have been proposed, there are still concerns about the application of medical deep learning approaches. This is due to some of medical imaging weaknesses, such...
Physically Adversarial Attacks and Defenses in Computer Vision: A Survey http://arxiv.org/abs/2211.01671 摘要 攻击:主要从攻击任务,攻击形式,攻击方法几个角度进行展开 防御:主要从预处理、在线处理、后处理等几个角度展开 介绍 电子对抗攻击:PGD [86], MI-FGSM [35], C&W [16], Deepfool [92] ...
Kaviani S, Han KJ, Sohn I (2022) Adversarial attacks and defenses on AI in medical imaging informatics: A survey. Expert Syst Appl 198(February 2021):116815 Article Google Scholar Kingma DP, Ba JL (2015) “Adam: A method for stochastic optimization,” 3rd Int. Conf. Learn. Represent. ...
4月,IJAC密歇根州立大学在最新一期发表Anil K. Jain团队和Jiliang Tang 密歇根州立大学团队合作带来的特别评论Xu Han博士。本文总结并讨论了与对抗样本及其应对策略相关的研究,系统全面地总结了图像、图形和文本领域的前沿算法,总结了对抗攻击和防御(adversarial attacks and defenses)主要技术和成果。
1Adversarial Examples: Attacks and Defenses forDeep LearningXiaoyong Yuan, Pan He, Qile Zhu, Rajendra Rana Bhat, Xiaolin LiNational Science Foundation Center for Big Learning, University of FloridaAbstract—With rapid progress and great successes in a widespectrum of applications, deep learning is ...
Tramèr, F, Kurakin A, Papernot N, Boneh D, McDaniel PD (2017) Ensemble adversarial training: Attacks and defenses. CoRR abs/1705.07204. 1705.07204. Vincent, P, Larochelle H, Bengio Y, Manzagol P-A (2008) Extracting and composing robust features with denoising autoencoders In: Proceedings ...