扩散驱动的欺骗性补丁：面部身份验证中的对抗操纵与取证检测
Diffusion-Driven Deceptive Patches: Adversarial Manipulation and Forensic Detection in Facial Identity Verification
AI总结：
本文提出了一种基于扩散模型的对抗性补丁生成与检测方法，通过FGSM和ViT-GPT2模型提升对抗性攻击的隐蔽性与有效性，并实现了高SSIM的检测性能。
摘要：
本文提出了一种端到端的流程，用于生成、细化和评估对抗性补丁以破坏面部生物识别系统，应用于犯罪分析和安全测试。我们利用FGSM生成针对身份分类器的对抗性噪声，并采用带有反向扩散的扩散模型，通过高斯平滑和自适应亮度校正来增强不可察觉性，从而促进合成对抗性补丁的规避。经过细化的补丁被应用于面部图像中，以测试其在维持自然视觉特征的同时规避识别系统的能力。一个Vision Transformer (ViT)-GPT2模型生成描述以提供对抗性图像中人物身份的语义描述，支持犯罪解释和记录以进行身份规避和识别攻击。该流程评估了在对抗条件下身份分类、描述结果以及面部身份验证和表情识别中的漏洞。我们进一步展示了使用感知哈希和分割技术有效检测和分析对抗性补丁和对抗性样本，实现了SSIM为0.95的性能。
英文摘要：
This work presents an end-to-end pipeline for generating, refining, and evaluating adversarial patches to compromise facial biometric systems, with applications in forensic analysis and security testing. We utilize FGSM to generate adversarial noise targeting an identity classifier and employ a diffusion model with reverse diffusion to enhance imperceptibility through Gaussian smoothing and adaptive brightness correction, thereby facilitating synthetic adversarial patch evasion. The refined patch is applied to facial images to test its ability to evade recognition systems while maintaining natural visual characteristics. A Vision Transformer (ViT)-GPT2 model generates captions to provide a semantic description of a person's identity for adversarial images, supporting forensic interpretation and documentation for identity evasion and recognition attacks. The pipeline evaluates changes in identity classification, captioning results, and vulnerabilities in facial identity verification and expression recognition under adversarial conditions. We further demonstrate effective detection and analysis of adversarial patches and adversarial samples using perceptual hashing and segmentation, achieving an SSIM of 0.95.
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。
