IDProtector: An Adversarial Noise Encoder to Protect Against ID-Preserving Image Generation
This addresses a privacy and security issue for individuals whose portrait photos are at risk of misuse by AI-generated identity-preserving content, representing an incremental advancement in adversarial protection techniques.
The paper tackles the problem of protecting portrait photos from unauthorized identity-preserving image generation by zero-shot methods like InstantID, introducing IDProtector, an adversarial noise encoder that applies imperceptible noise to achieve universal protection against multiple state-of-the-art encoder-based methods while maintaining robustness to common image transformations.
Recently, zero-shot methods like InstantID have revolutionized identity-preserving generation. Unlike multi-image finetuning approaches such as DreamBooth, these zero-shot methods leverage powerful facial encoders to extract identity information from a single portrait photo, enabling efficient identity-preserving generation through a single inference pass. However, this convenience introduces new threats to the facial identity protection. This paper aims to safeguard portrait photos from unauthorized encoder-based customization. We introduce IDProtector, an adversarial noise encoder that applies imperceptible adversarial noise to portrait photos in a single forward pass. Our approach offers universal protection for portraits against multiple state-of-the-art encoder-based methods, including InstantID, IP-Adapter, and PhotoMaker, while ensuring robustness to common image transformations such as JPEG compression, resizing, and affine transformations. Experiments across diverse portrait datasets and generative models reveal that IDProtector generalizes effectively to unseen data and even closed-source proprietary models.