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Diffusion Attack: Leveraging Stable Diffusion for Naturalistic Image Attacking
Conference proceeding   Peer reviewed

Diffusion Attack: Leveraging Stable Diffusion for Naturalistic Image Attacking

Qianyu Guo, Jiaming Fu, Yawen Lu and Dongming Gan
2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), pp.975-976
Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) (Orlando, Florida, USA, 03/16/2024–03/21/2024)
03/16/2024
DOI: https://doi.org/10.1109/VRW62533.2024.00284
Web of Science ID: WOS:001239375400278

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2 Times Cited - Web of Science

Abstract

In Virtual Reality (VR), adversarial attack remains a significant se-curity 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 no-ticeable 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.

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