However, recent studies uncover that they are extremely vulnerable to adversarial structural perturbations, leading to their outcomes unreliable. In this paper, we propose DefenseVGAE, a novel defense method for leveraging variational graph autoencoders (VGAEs) to defend GNNs against such attacks. ...
2020年NIPS的文章,旨在解决目前的图神经网络算法对存在扰动的网络输入,结果表现很差的问题。(GNNGUARD: a general algorithm to defend against a variety of training-time attacks that perturb the discrete graph structure. )先从实验结果说,这篇文章结果太秀了。(PS:刚去简单搜了一下知乎还没人写过这篇,哈哈...
《All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs》 厦明大 DOTA2人机GG君,想去画画唱民谣弹吉他的码农6 人赞同了该文章 最新的图对抗网络很多篇文章都使用了这篇文章的结论,因此很值得看一下这篇文章的工作。这篇文章的发现是具有意义的,虽然该篇文章有不少理论使用了其他文章...
D EFENDINGA GAINST A DVERSARIAL A TTACKSShai RozenbergTechnion –Israel Institute of Technologyshairoz@cs.technion.ac.ilGal ElidanGoogle Researchelidan@google.comRan El-YanivTechnionrani@cs.technion.ac.ilA BSTRACTThis paper is concerned with the defense of deep models against adversarial attacks....
To achieve adversarial defense without changing the instances as well as the detectors, a novel defensive paradigm called Inspector is designed specifically for face forgery detectors. Specifically, Inspector defends against adversarial attacks in a coarse-to-fine manner. In the coarse defense stage, ...
This mechanism utilizes a modified ResNet model that defends against adversarial attacks. We deploy it on the edge cloud to preprocess the data uploaded by metaverse AI applications. In order to achieve a better model performance, we use multiple residual network blocks to build this neural ...
the federated setting makes the model vulnerable to various adversarial attacks in the presence of malicious clients. Despite the theoretical and empirical success in defending against attacks that aim to degrade models’ utility, defense against backdoor attacks that increase model accuracy...
Bhagoji, A.N., Chakraborty, S., Mittal, P., Calo, S.: Analyzing federated learning through an adversarial lens. In: the 36th International Conference on Machine Learning (2019) Google Scholar Biggio, B., Nelson, B., Laskov, P.: Poisoning attacks against support vector machines. In: Pr...
Although deep neural networks (DNNs) have achieved a great success in various computer vision tasks, it is recently found that they are vulnerable to adversarial attacks. In this paper, we focus on the so-called extit{backdoor attack}, which injects a backdoor trigger to a small portion of...
The accuracy under the deployed defense on practical datasets is nearly unchanged when operating in the absence of attacks. The accuracy of a model trained using Auror drops by only 3% even when 30% of all the users are adversarial. Auror provides a strong guarantee against evasion; if the ...