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.

翻译：近年来，如InstantID等零样本方法彻底改变了身份保持生成技术。与DreamBooth等多图像微调方法不同，这些零样本方法利用强大的人脸编码器从单张肖像照片中提取身份信息，通过单次推理即可实现高效的身份保持生成。然而，这种便利性也给面部身份保护带来了新的威胁。本文旨在保护肖像照片免遭基于编码器的未授权定制。我们提出了IDProtector，这是一种对抗性噪声编码器，可在单次前向传播中对肖像照片施加难以察觉的对抗性噪声。我们的方法为肖像提供了针对多种基于编码器的最新方法（包括InstantID、IP-Adapter和PhotoMaker）的通用保护，同时确保了对常见图像变换（如JPEG压缩、尺寸调整和仿射变换）的鲁棒性。在不同肖像数据集和生成模型上的实验表明，IDProtector能够有效泛化至未见数据，甚至对闭源专有模型也表现出良好的泛化能力。