Abstract
Adversarial attacks against computer vision systems have emerged as a critical research area that challenges the fundamental assumptions about neural network robustness and security. This comprehensive survey examines the evolving landscape of adversarial techniques, revealing their dual nature as both sophisticated security threats and valuable defensive tools. We provide a systematic analysis of adversarial attack methodologies across three primary domains: pixel-space attacks, physically realizable attacks, and latent-space attacks. Our investigation traces the technical evolution from early gradient-based methods such as FGSM and PGD to sophisticated optimization techniques incorporating momentum, adaptive step sizes, and advanced transferability mechanisms. We examine how physically realizable attacks have successfully bridged the gap between digital vulnerabilities and real-world threats through adversarial patches, 3D textures, and dynamic optical perturbations. Additionally, we explore the emergence of latent-space attacks that leverage semantic structure in internal representations to create more transferable and meaningful adversarial examples. Beyond traditional offensive applications, we investigate the constructive use of adversarial techniques for vulnerability assessment in biometric authentication systems and protection against malicious generative models. Our analysis reveals critical research gaps, particularly in neural style transfer protection and computational efficiency requirements. This survey contributes a comprehensive taxonomy, evolution analysis, and identification of future research directions, aiming to advance understanding of adversarial vulnerabilities and inform the development of more robust and trustworthy computer vision systems.
Abstract (translated)
针对计算机视觉系统的对抗攻击已发展成为一项关键研究领域，挑战了关于神经网络鲁棒性和安全性的基本假设。这篇全面的综述考察了对抗技术不断演变的格局，并揭示了它们作为复杂的安全威胁和有价值的防御工具的双重性质。我们系统地分析了三种主要领域的对抗性攻击方法：像素空间攻击、物理上可实现的攻击以及潜在空间攻击。 我们的研究追溯了从早期基于梯度的方法（如FGSM和PGD）到包含动量、自适应步长和先进转移机制等更复杂优化技术的技术演变。我们还探讨了如何通过对抗性贴纸、3D纹理和动态光学扰动，物理上可实现的攻击成功地将数字漏洞与现实世界威胁联系起来。 此外，我们研究了潜在空间攻击的出现，这些攻击利用内部表示中的语义结构来创建更具转移性和意义的对抗样本。除了传统的进攻应用之外，我们还调查了如何利用对抗性技术进行生物特征认证系统中的脆弱性评估以及防范恶意生成模型。 我们的分析揭示了一些关键的研究空白，特别是在神经风格转换保护和计算效率要求方面。这份综述贡献了一个全面的技术分类、演进分析，并识别出未来研究的方向，旨在深化对对抗性漏洞的理解，并促进开发更加稳健且可信的计算机视觉系统。
URL
https://arxiv.org/abs/2508.01845
