ID-Guard：一种通过破坏身份特征来对抗面部操纵的通用框架
ID-Guard: A Universal Framework for Combating Facial Manipulation via Breaking Identification
AI总结：
本研究针对面部操纵滥用的问题，提出ID-Guard通用框架，通过身份破坏模块生成跨模型可迁移扰动，可防御多种操纵模型并集成到其他任务中。
摘要：
基于深度学习的面部操纵技术的滥用对公民权利构成严重威胁。为从源头防范此类欺诈，研究人员提出了主动防御方法，将不可见的对抗扰动嵌入图像中，以干扰操纵过程，使生成的伪造输出无法被观察者信服。然而，对输出的无目标干扰可能会保留可识别的面部特征，从而可能导致个体被污名化。在本研究中，我们提出了一种用于对抗面部操纵的通用框架，命名为ID-Guard。该框架采用编码器-解码器网络的单次前向传播来生成跨模型可迁移的对抗扰动。我们引入了一种新颖的身份破坏模块（Identity Destruction Module, IDM），以抑制被操纵面部中的可识别特征。扰动生成通过将各种操纵类型的破坏表述为多任务学习问题进行优化，其中设计了一种动态加权策略以提升跨模型性能。实验结果表明，ID-Guard可有效防御多种面部操纵模型，同时降低被操纵图像中可识别区域的辨识度，还能使被干扰的图像逃避面部修复和人脸识别系统的检测。此外，ID-Guard可作为即插即用组件无缝集成到其他任务中，例如对抗训练。源代码可在该https URL处获取。
英文摘要：
The misuse of deep learning-based facial manipulation poses a serious threat to civil rights. To prevent such fraud at its source, proactive defense methods have been proposed that embed invisible adversarial perturbations into images, disrupting the manipulation process and rendering the forged output unconvincing to observers. However, non-targeted disruption of the output may leave identifiable facial features intact, potentially leading to the stigmatization of individuals. In this work, we propose a universal framework for combating facial manipulation, termed ID-Guard. The framework employs a single forward pass of an encoder-decoder network to generate cross-model transferable adversarial perturbations. We introduce a novel Identity Destruction Module (IDM) to suppress identifiable features in manipulated faces. The perturbation generation is optimized by formulating the disruption of various manipulation types as a multi-task learning problem, with a dynamic weighting strategy designed to enhance cross-model performance. Experimental results show that ID-Guard effectively defends against diverse facial manipulation models while degrading identifiable regions in manipulated images. It also enables disrupted images to evade facial inpainting and facial recognition systems. Moreover, ID-Guard can be seamlessly integrated as a plug-and-play component into other tasks, such as adversarial training. The source code is publicly available at https://github.com/ZOMIN28/ID-Guard.
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。
