Diffusion Attack：利用 Stable Diffusion 进行自然风格图像攻击
Diffusion Attack: Leveraging Stable Diffusion for Naturalistic Image Attacking
AI总结：
针对VR中对抗攻击图像不自然易被察觉的问题，提出结合风格迁移的框架，制作兼具低检测率、高自然外观和强攻击能力的对抗输入。
摘要：
在虚拟现实（VR）中，对抗攻击仍然是重大的安全威胁。大多数基于深度学习的物理和数字对抗攻击方法，通过制作包含大型可打印扭曲的对抗样本（这种扭曲容易被人类观察者识别）来提升攻击性能。然而，攻击者很少对生成攻击图像的外观自然性和舒适度施加限制，导致攻击明显且不自然。为了应对这一挑战，我们提出一个框架，结合风格迁移来制作自然风格的对抗输入，展现出最低的可检测性和最高的自然外观，同时保持卓越的攻击能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
