High Quality Face De-identification with Model Explainability

Face de-identification involves concealing the true identity of a face while retaining other facial characteristics. Current target-generic methods typically disentangle identity features in the latent space, using adversarial training to balance privacy and utility. However, this pattern often leads to a tradeoff between privacy and utility, and the latent space remains difficult to explain. To address these issues, we propose IDeudemon, which employs a “divide and conquer” strategy to protect identity and preserve utility step by step while maintaining good explainability. In Step I, we obfuscate the three-dimensional (3D) disentangled ID code calculated by a parametric neural radiance field (NeRF) model to protect identity. In Step II, we incorporate visual similarity assistance and train a generative adversarial network (GAN) with adjusted losses to preserve image utility. Thanks to the powerful 3D prior and delicate generative designs, our approach could protect the identity naturally, produce high quality details, and is robust to different poses and expressions. Extensive experiments demonstrate that the proposed IDeudemon outperforms previous state-of-the-art methods.