IDProtector: An Adversarial Noise Encoder to Protect Against ID-Preserving Image Generation
Yiren Song, Pei Yang, Hai Ci, Mike Zheng Shou

TL;DR
IDProtector is an adversarial noise encoder designed to protect portrait photos from unauthorized identity-preserving image generation methods, ensuring privacy against state-of-the-art encoder-based attacks with robustness to common image transformations.
Contribution
The paper introduces IDProtector, a novel adversarial noise method that provides universal, imperceptible protection for portraits against multiple encoder-based generation techniques.
Findings
IDProtector effectively blocks multiple state-of-the-art encoder-based methods.
It maintains robustness against JPEG compression, resizing, and affine transformations.
The approach generalizes well to unseen data and proprietary models.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdversarial Robustness in Machine Learning · Digital Media Forensic Detection · Generative Adversarial Networks and Image Synthesis
