In this way, it is a Generalized Defense against adversarial attacks, capable of defending any classifier model against any attacks. This enables the user to directly integrate CD-GAN with an existing production deployed classifier smoothly. CD-GAN iteratively removes the adversarial noise using a ...
Defense against adversarial attacks in nlp via dirichlet neighborhood ensemble[J]. arXiv preprint arXiv:2006.11627, 2020. [2] Alzantot M, Sharma Y, Elgohary A, et al. Generating Natural Language Adversarial Examples[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language...
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training阅读 wastelands 珂学家 来自专栏 · 对抗防御 1 人赞同了该文章 摘要 我们引入了一种基于特征散射的对抗性训练方法,以提高模型对对抗性攻击的鲁棒性。在潜在空间通过特征散射生成对抗样本,实质上是一种无监督学习方法,还可以避免标...
More information:Bridging machine learning and cryptography in defence against adversarial attacks. arXiv:1809.01715v1 [cs.CR].arxiv.org/abs/1809.01715 Abstract In the last decade, deep learning algorithms have become very popular thanks to the achieved performance in many machine learning and computer...
The rise of Adversarial Machine Learning (AML) attacks is presenting a significant challenge to Intrusion Detection Systems (IDS) and their ability to detect threats. To address this issue, we introduce Apollon, a novel defense system that can protect IDS against AML attacks. Apollon utilizes a ...
In recent years, the growing vulnerability of deep neural networks (DNNs) to adversarial attacks has posed sig-nificant challenges in the field of machine learning, particularly in mission-critical applications such as computer vision. As a result, adversarial machine learning has emerged as a cruci...
Samangouei P, Kabkab M, Chellappa R, et al. Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models.[J]. arXiv: Comput
Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks论文阅读 这篇论文发表在 2019 ICCV ,The IEEE International Conference on Computer Vision (ICCV) 基于深度学习的超分辨率重建的方法由称为超分辨率卷积神经网络(SRCNN)的简单卷积网络模型触发的,可以提供更高质量的放大图像。 超分辨率技...
Adversarial patch attacks create adversarial examples by injecting arbitrary distortions within a bounded region of the input to fool deep neural networks (DNNs). These attacks are robust (i.e., physically-realizable) and universally malicious, and hence represent a severe security threat to real-...
SpecRNet0.68330.73940.7614 RawNet30.74300.82650.8549 Table 2: Transferability attacks results. TM denotes the target model, whereasAM— attack model. We report parameter values in rows (in increasing order). The values in bold denote EERs between smaller architectures. ...