SCOL: Style Code Orchestration in Latent Space for Proactive Face-Swapping Defense

摘要

Face-swapping deepfake poses significant risks, including privacy violations, misinformation, and defamation, amplified by the availability of pretrained models on open-source platforms. Proactive defense strategies aim to disrupt deepfake generation by modifying the original images to protect identity features. However, existing methods often introduce artifacts in facial images or rely on specific deepfake models, limiting their usability. To address these problems, we propose a style code orchestration in latent space (SCOL) method that obfuscates identity by fusing different identities in the latent space without requiring face recognition models. This study optimizes the generator to follow the original appearance while retaining the obfuscated identity via identity-preserving constraints. Further, appearance-dominant components in the latent code are aligned for visual consistency. An identity inversion attack is introduced using opposite style codes to improve the effectiveness of the defense. Experimental results demonstrate that SCOL robustly defends against various face-swapping deepfake methods, maintaining visual consistency.