Deepfake, an AI-driven face-swapping technique, has been weaponized to spread disinformation. In response, researchers have developed forensic detectors to identify such manipulations. To circumvent these defenses, a growing body of work now focuses on generating adversarial samples—carefully perturbed forgeries designed to deceive detection tools. However, most existing adversarial generation methods sacrifice image quality to achieve undetectability, introducing perceptible artifacts that ironically make them more detectable under human scrutiny. To address this limitation, we propose a novel spectral fusion approach to multimodally synthesize forgery traces from authentic facial images. Unlike traditional noise injection methods, our technique integrates diffusion-based noise during image preprocessing, embedding perturbations in the forward process of a diffusion model. This approach not only deceives forensic detectors more effectively but also preserves high visual fidelity. Through extensive experiments, our method achieves state-of-the-art DeepFake anti-forensic performance while preserving high visual fidelity, ensuring that the adversarial samples remain indistinguishable from real images.
Ding et al. (Sat,) studied this question.
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