ID-Guard: A Universal Framework for Combating Facial Manipulation via Breaking Identification
Zuomin Qu, Wei Lu, Xiangyang Luo, Qian Wang, Xiaochun Cao

TL;DR
ID-Guard is a universal framework that uses adversarial perturbations to disrupt facial manipulation, degrading identifiable features and preventing forgery detection while maintaining cross-model effectiveness.
Contribution
It introduces a novel Identity Destruction Module and a multi-task learning approach for universal facial manipulation defense with high transferability.
Findings
Effective against various facial manipulation models
Degrades identifiable facial features in manipulated images
Evasion of facial recognition and inpainting systems
Abstract
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…
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Taxonomy
TopicsFace recognition and analysis
