Diffusion Attack: Leveraging Stable Diffusion for Naturalistic Image
  Attacking
Qianyu Guo Jiaming Fu Yawen Lu Dongming Gan
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.