DiffMI: Breaking Face Recognition Privacy via Diffusion-Driven Training-Free Model Inversion

Summary

DiffMI is a diffusion-driven, training-free model inversion attack that recovers identity information from face recognition systems that map facial images to embeddings. The method combines latent code initialization, ranked adversarial refinement, and a confidence-aware optimization objective, and it applies directly to unseen target identities. The authors report 84.42%–92.87% attack success rates against inversion-resilient systems, 4.01%–9.82% higher than the best prior training-free GAN-based approach. The implementation is available at https://github.com/azrealwang/DiffMI.

Topics

• Training data and privacy