ID-Guard: A Universal Framework for Combating Facial Manipulation via Breaking Identification

The misuse of deep learning-based facial manipulation poses a significant threat to civil rights. To prevent this fraud at its source, proactive defense has been proposed to disrupt the manipulation process by adding invisible adversarial perturbations into images, making the forged output unconvincing to observers. However, the non-specific disruption against the output may lead to the retention of identifiable facial features, potentially resulting in the stigmatization of the individual. This paper proposes a universal framework for combating facial manipulation, termed ID-Guard. Specifically, this framework operates with a single forward pass of an encoder-decoder network to produce a cross-model transferable adversarial perturbation. A novel Identity Destruction Module (IDM) is introduced to degrade identifiable features in forged faces. We optimize the perturbation generation by framing the disruption of different facial manipulations as a multi-task learning problem, and a dynamic weight strategy is devised to enhance cross-model performance. Experimental results demonstrate that the proposed ID-Guard exhibits strong efficacy in defending against various facial manipulation models, effectively degrading identifiable regions in manipulated images. It also enables disrupted images to evade facial inpainting and image recognition systems. Additionally, ID-Guard can seamlessly function as a plug-and-play component, integrating with other tasks such as adversarial training.

@article{qu2025_2409.13349,
  title={ ID-Guard: A Universal Framework for Combating Facial Manipulation via Breaking Identification },
  author={ Zuomin Qu and Wei Lu and Xiangyang Luo and Qian Wang and Xiaochun Cao },
  journal={arXiv preprint arXiv:2409.13349},
  year={ 2025 }
}