归因即检索：模型无关的AI生成图像溯源
Attribution as Retrieval: Model-Agnostic AI-Generated Image Attribution
把AI生成图像溯源从闭集分类改写为实例检索，配合低比特平面指纹与无监督预训练+少样本适配，使视觉取证系统无需访问生成模型即可泛化到未见过的生成器，可直接复用于深度伪造检测与来源归因。
原文依据 formulating it as an instance retrieval problem instead of a conventional image classification problem
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论随着AIGC技术的快速发展，图像取证将面临前所未有的挑战。传统方法无法应对由快速演进的图像生成技术所产生的日益逼真的图像。为便于识别AI生成图像并溯源其来源模型，生成式图像水印与AI生成图像溯源近年来成为关键研究焦点。然而，现有方法依赖模型，需要访问生成模型，且对新的、未见过的生成器缺乏通用性与可扩展性。为解决这些局限，本工作提出一种AI生成图像溯源的新范式，将其建模为实例检索问题而非传统的图像分类问题。我们提出一种高效的模型无关框架，称为基于低比特平面的深度伪造溯源（LIDA）。LIDA的输入由低比特指纹生成模块产生，其训练包括无监督预训练以及随后的少样本溯源适配。综合实验表明，LIDA在零样本与少样本设定下的深度伪造检测和图像溯源两方面均达到最优性能。代码见 https://github.com/hongsong-wang/LIDA
英文原文摘要 1338 字符
With the rapid advancement of AIGC technologies, image forensics will encounter unprecedented challenges. Traditional methods are incapable of dealing with increasingly realistic images generated by rapidly evolving image generation techniques. To facilitate the identification of AI-generated images and the attribution of their source models, generative image watermarking and AI-generated image attribution have emerged as key research focuses in recent years. However, existing methods are model-dependent, requiring access to the generative models and lacking generality and scalability to new and unseen generators. To address these limitations, this work presents a new paradigm for AI-generated image attribution by formulating it as an instance retrieval problem instead of a conventional image classification problem. We propose an efficient model-agnostic framework, called Low-bIt-plane-based Deepfake Attribution (LIDA). The input to LIDA is produced by Low-Bit Fingerprint Generation module, while the training involves Unsupervised Pre-Training followed by subsequent Few-Shot Attribution Adaptation.Comprehensive experiments demonstrate that LIDA achieves state-of-the-art performance for both Deepfake detection and image attribution under zero- and few-shot settings. The code is at https://github.com/hongsong-wang/LIDA