通过概率比检测自回归生成图像，可准确识别来源模型。
PRADA: Probability-Ratio-Based Attribution and Detection of Autoregressive-Generated Images
- 计算图像生成时的条件与无条件概率比，捕捉模型特异性特征。
- 在8个类到图像和4个文本到图像模型上均实现高精度检测。
- 方法简单可解释，适合需要溯源与可信生成的场景。
自回归（AR）图像生成近期成为强大的图像合成范式，其原理借鉴大型语言模型，能高效生成逼真的图像，进一步加剧了对可靠检测方法的需求。然而，目前尚无专门针对自回归图像生成器生成图像的检测工作。本文提出PRADA（基于概率比的自回归生成图像归因与检测），一种简单且可解释的方法，能够可靠检测自回归生成图像并将其归因至具体源模型。核心思想是检查给定图像对应的自回归标记序列在特定模型下的条件概率与无条件概率之比。当图像由某模型生成时，其概率比呈现独特特征，而真实图像或其他模型生成的图像则不具备该特征。我们通过校准简单的、模型相关的得分函数，利用这些特征进行基于阈值的归因与检测。实验评估表明，PRADA在八个类到图像和四个文本到图像模型上表现优异。代码与数据已开源至github.com/jonasricker/prada。
原文摘要 · Abstract (English)
Autoregressive (AR) image generation has recently emerged as a powerful paradigm for image synthesis. Leveraging the generation principle of large language models, they allow for efficiently generating deceptively real-looking images, further increasing the need for reliable detection methods. However, to date there is a lack of work specifically targeting the detection of images generated by AR image generators. In this work, we present PRADA (Probability-Ratio-Based Attribution and Detection of Autoregressive-Generated Images), a simple and interpretable approach that can reliably detect AR-generated images and attribute them to their respective source model. The key idea is to inspect the ratio of a model's conditional and unconditional probability for the autoregressive token sequence representing a given image. Whenever an image is generated by a particular model, its probability ratio shows unique characteristics which are not present for images generated by other models or real images. We exploit these characteristics for threshold-based attribution and detection by calibrating a simple, model-specific score function. Our experimental evaluation shows that PRADA is highly effective against eight class-to-image and four text-to-image models. We release our code and data at github.com/jonasricker/prada.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。