My Face Is Mine, Not Yours: Facial Protection Against Diffusion Model Face Swapping
It addresses the risk of unethical face swapping for individuals, but is incremental as it builds on existing adversarial methods for diffusion models.
This paper tackles the problem of unauthorized facial image manipulation by diffusion-based deepfake systems by introducing a proactive defense strategy using adversarial attacks, achieving a 95% success rate in protecting images against state-of-the-art models.
The proliferation of diffusion-based deepfake technologies poses significant risks for unauthorized and unethical facial image manipulation. While traditional countermeasures have primarily focused on passive detection methods, this paper introduces a novel proactive defense strategy through adversarial attacks that preemptively protect facial images from being exploited by diffusion-based deepfake systems. Existing adversarial protection methods predominantly target conventional generative architectures (GANs, AEs, VAEs) and fail to address the unique challenges presented by diffusion models, which have become the predominant framework for high-quality facial deepfakes. Current diffusion-specific adversarial approaches are limited by their reliance on specific model architectures and weights, rendering them ineffective against the diverse landscape of diffusion-based deepfake implementations. Additionally, they typically employ global perturbation strategies that inadequately address the region-specific nature of facial manipulation in deepfakes.