Generating Adversarial Attacks in the Latent Space
Adversarial attacks in the input (pixel) space typically incorporate noise margins such as $L_1$ or $L_{\infty}$-norm to produce imperceptibly perturbed data that confound deep learning networks. Such noise margins confine the magnitude of permissible noise. In this work, we propose injecting adversarial perturbations in the latent (feature) space using a generative adversarial network, removing the need for margin-based priors. Experiments on MNIST, CIFAR10, Fashion-MNIST, CIFAR100 and Stanford Dogs datasets support the effectiveness of the proposed method in generating adversarial attacks in the latent space while ensuring a high degree of visual realism with respect to pixel-based adversarial attack methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Adversarial AttackGenerative Adversarial NetworkSimilar Papers 제목 키워드 기반
Generating Out of Distribution Adversarial Attack using Latent Space Poisoning
Traditional adversarial attacks rely upon the perturbations generated by gradients from the network which are generally safeguarded by gradient guided search to provide an adversarial counterpart to the network. In this …
Adversarial AttackGenerating Realistic Adversarial Examples for Business Processes using Variational Autoencoders
In predictive process monitoring, predictive models are vulnerable to adversarial attacks, where input perturbations can lead to incorrect predictions. Unlike in computer vision, where these perturbations are designed to…
Predictive Process MonitoringShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds
We introduce ShapeAdv, a novel framework to study shape-aware adversarial perturbations that reflect the underlying shape variations (e.g., geometric deformations and structural differences) in the 3D point cloud space. …
An h-space Based Adversarial Attack for Protection Against Few-shot Personalization
The versatility of diffusion models in generating customized images from few samples raises significant privacy concerns, particularly regarding unauthorized modifications of private content. This concerning issue has re…
Adversarial AttackImage GenerationGenerating Semantic Adversarial Examples via Feature Manipulation
The vulnerability of deep neural networks to adversarial attacks has been widely demonstrated (e.g., adversarial example attacks). Traditional attacks perform unstructured pixel-wise perturbation to fool the classifier. …
Adversarial AttackDisentanglement