Diffusion Attack: Leveraging Stable Diffusion for Naturalistic Image Attacking
In Virtual Reality (VR), adversarial attack remains a significant security threat. Most deep learning-based methods for physical and digital adversarial attacks focus on enhancing attack performance by crafting adversarial examples that contain large printable distortions that are easy for human observers to identify. However, attackers rarely impose limitations on the naturalness and comfort of the appearance of the generated attack image, resulting in a noticeable and unnatural attack. To address this challenge, we propose a framework to incorporate style transfer to craft adversarial inputs of natural styles that exhibit minimal detectability and maximum natural appearance, while maintaining superior attack capabilities.
Code (0)
등록된 구현이 없습니다.
Tasks
Adversarial AttackStyle TransferMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion
Despite the record-breaking performance in Text-to-Image (T2I) generation by Stable Diffusion, less research attention is paid to its adversarial robustness. In this work, we study the problem of adversarial attack gener…
Adversarial AttackAdversarial RobustnessAdversarial TextDiffusion to Confusion: Naturalistic Adversarial Patch Generation Based on Diffusion Model for Object Detector
Many physical adversarial patch generation methods are widely proposed to protect personal privacy from malicious monitoring using object detectors. However, they usually fail to generate satisfactory patch images in ter…
DiffPatch: Generating Customizable Adversarial Patches using Diffusion Model
Physical adversarial patches printed on clothing can easily allow individuals to evade person detectors. However, most existing adversarial patch generation methods prioritize attack effectiveness over stealthiness, resu…
modelImproving Adversarial Transferability by Stable Diffusion
Deep neural networks (DNNs) are susceptible to adversarial examples, which introduce imperceptible perturbations to benign samples, deceiving DNN predictions. While some attack methods excel in the white-box setting, the…
Attack-Resilient Image Watermarking Using Stable Diffusion
Watermarking images is critical for tracking image provenance and proving ownership. With the advent of generative models, such as stable diffusion, that can create fake but realistic images, watermarking has become part…
Denoising