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

As deepfake technologies continue to evolve, proactive defense techniques have gained increasing attention for their potential to either neutralize deepfake operations or simplify detection through pre-embedded signals. In this paper, inspired by watermark-based forensic methods, we explore a novel detection framework built on the concept of “hiding a learnable face within a face”. Specifically, we use a semi-fragile invertible steganography network to imperceptibly embed a learnable template image within a host face image to be protected. This template serves as an indicator revealing signs of tampering when recovered through the inverse steganography process. Unlike manually designed templates, it is optimized during training to resemble a neutral facial appearance—functioning like a subtle “big brother” hidden within the image. Through a self-blending mechanism and robustness learning strategy with a simulated transmission channel, we develop a robust detector that accurately distinguishes between malicious tampering and benign processing of the steganographic image. Extensive experiments across multiple datasets validate the superiority of the proposed approach over competing passive and proactive detection methods.