Big Brother is Watching: Proactive Deepfake Detection via Learnable Hidden Face

• URL: http://arxiv.org/abs/2504.11309v1
• Date: Tue, 15 Apr 2025 15:50:54 GMT
• Title: Big Brother is Watching: Proactive Deepfake Detection via Learnable Hidden Face
• Authors: Hongbo Li, Shangchao Yang, Ruiyang Xia, Lin Yuan, Xinbo Gao,
• Abstract summary: Secret template image is embedded into a host image imperceptibly, acting as an indicator monitoring for any malicious image forgery.<n>A robust detector is built to accurately distinguish if the steganographic image is maliciously tampered or benignly processed.
• Score: 40.40196403891759
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As deepfake technologies continue to advance, passive detection methods struggle to generalize with various forgery manipulations and datasets. Proactive defense techniques have been actively studied with the primary aim of preventing deepfake operation effectively working. In this paper, we aim to bridge the gap between passive detection and proactive defense, and seek to solve the detection problem utilizing a proactive methodology. Inspired by several watermarking-based forensic methods, we explore a novel detection framework based on the concept of ``hiding a learnable face within a face''. Specifically, relying on a semi-fragile invertible steganography network, a secret template image is embedded into a host image imperceptibly, acting as an indicator monitoring for any malicious image forgery when being restored by the inverse steganography process. Instead of being manually specified, the secret template is optimized during training to resemble a neutral facial appearance, just like a ``big brother'' hidden in the image to be protected. By incorporating a self-blending mechanism and robustness learning strategy with a simulative transmission channel, a robust detector is built to accurately distinguish if the steganographic image is maliciously tampered or benignly processed. Finally, extensive experiments conducted on multiple datasets demonstrate the superiority of the proposed approach over competing passive and proactive detection methods.

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