DeepProtect: Proactive Face-Swapping Defense using Identity Blending and Attribute Distortion

Face-swapping deepfakes allow realistic identity transfer, which can serve creative purposes but increases the risk of identity abuse. A proactive defense aims to prevent deepfake creation by obstructing identity feature extraction from input images, essential for identity-driven face-swapping. Existing proactive defense approaches aim to protect faces by hindering accurate identity feature extraction, but tend to introduce visible artifacts and fail to degrade the visual quality of the face-swapping deepfakes. This work proposes a proactive face-swapping defense using identity blending and attribute distortion (DeepProtect) that integrates global identity fusion in the latent space and local prompt-driven adversarial watermarking to address these problems. This work dilutes distinct identity representations by channel-wise blending of multiple identities in the latent space and optimizing the generator for visual consistency. The proposed approach distorts facial components in the identity space, directly influencing how faces are reconstructed in deepfakes. This approach applies semantic directions derived from user-provided text prompts to embed imperceptible adversarial watermarks that selectively distort facial attributes, affecting the visual fidelity of deepfake results. The proposed method hinders face-swapping deepfakes while preserving the perceptual quality of the protected images, offering a robust and practical solution for facial privacy protection. The experimental results reveal that DeepProtect effectively defends against face-swapping deepfakes while preserving visual consistency.