DiffProtect: Generative adversarial examples using diffusion models for facial privacy protection

• Diffusion model-based adversarial attacks for facial privacy protection with high visual quality. • Face semantics regularization module preserves visual identity during facial privacy protection. • Attack acceleration strategy significantly improves efficiency while maintaining performance. • 24.5 % absolute improvement in attack success rate compared to state-of-the-art methods. • Real-world validation with commercial API and user study shows practical effectiveness. The increasingly pervasive facial recognition (FR) systems raise serious concerns about personal privacy, especially for billions of users who have publicly shared their photos on social media. To address this challenge, several adversarial attack methods have been proposed to protect individuals from being identified by unauthorized FR systems with perturbed facial images. However, these approaches suffer from poor visual quality or low attack success rates, which limit their practical utility. Recently, diffusion models have achieved tremendous success in image generation. In this work, we ask: can diffusion models be used to generate adversarial examples against FR systems to improve both visual quality and attack performance? We propose DiffProtect, a novel method leveraging a diffusion autoencoder to generate semantically meaningful perturbations on FR systems. Extensive experiments demonstrate that DiffProtect produces more natural-looking encrypted images than state-of-the-art methods while achieving significantly higher attack success rates, e.g. , 24.5 % and 25.1 % absolute improvements on the CelebA-HQ and FFHQ datasets. We further evaluate the effectiveness of DiffProtect in the real world using a commercial FR API and validate its usefulness in practice through a user study. Our code is available at https://github.com/joellliu/DiffProtect .