EA-APO: A Universal Proactive Defense Against Facial Manipulation

The advent of deep learning has accelerated the development of facial manipulation techniques, particularly face-swapping and face attribute editing, raising serious concerns about privacy and identity-related misuse. Existing proactive defense methods predominantly target attribute editing and often generalize poorly to face-swapping models, making it difficult to provide effective protection across both tasks within a unified framework. To bridge this gap, we propose a generalized defense framework, Epoch-Adaptive Adversarial Perturbation Optimization (EA-APO). Specifically, EA-APO introduces a proactive defense mechanism that establishes optimal adversarial paths by optimizing perturbations on a white-box surrogate model to enhance adversarial transferability, and applies the resulting perturbations to source face images to disrupt both face swapping and face attribute editing, even against previously unseen target models in black-box settings. This approach mitigates identity feature tampering while adapting to changes in visual attributes and preserving high-quality adversarial examples. Experimental results show the generalization of our method across multiple face-swapping and attribute-editing models, including commercial ones, while also maintaining strong defense under various common post-processing operations and real-world social media transmission conditions, underscoring its potential for real-world deployment.