通过融合多版近似重复图像质量信息，提升真实网络环境中生成图像检测的可靠性。
Quality-Aware Calibration for AI-Generated Image Detection in the Wild
- 基于近似重复图像的多版本输入，结合质量评估进行加权融合决策
- 在多个主流检测器上平均提升8%的平衡准确率，显著优于简单平均
- 适用于需要高鲁棒性的真实场景生成内容检测，如社交媒体审核
尽管合成图像检测技术取得显著进展，但现有方法大多仅针对单张图像，忽视了真实网络传播中的关键特征：病毒式传播导致同一图像产生多个近似重复版本，并因反复压缩、缩放和裁剪而降低质量。这使得同一图像在不同版本下可能得出不一致的取证判断。为此，本文提出QuAD（Quality-Aware calibration with near-Duplicates）框架，基于同一图像的所有近似重复版本进行联合决策。给定查询图像，系统检索其在线近似重复项并输入检测器，再根据各版本估计的质量进行加权聚合。该方法充分利用全部信息，同时考虑多重处理后图像可信度下降的问题。为支持大规模评估，我们构建两个数据集：AncesTree（13.6万张图像，按随机降级树模拟传播动态）和ReWIND（近1万张从真实病毒内容中采集的近似重复图像）。在多个前沿检测器上的实验表明，质量感知融合使性能持续提升，平均平衡准确率提高约8%（相较简单平均）。结果强调了在真实应用中联合处理所有可用在线图像对可靠检测生成内容的重要性。代码与数据已公开于https://grip-unina.github.io/QuAD/
原文摘要 · Abstract (English)
Significant progress has been made in detecting synthetic images, however most existing approaches operate on a single image instance and overlook a key characteristic of real-world dissemination: as viral images circulate on the web, multiple near-duplicate versions appear and lose quality due to repeated operations like recompression, resizing and cropping. As a consequence, the same image may yield inconsistent forensic predictions based on which version has been analyzed. In this work, to address this issue we propose QuAD (Quality-Aware calibration with near-Duplicates) a novel framework that makes decisions based on all available near-duplicates of the same image. Given a query, we retrieve its online near-duplicates and feed them to a detector: the resulting scores are then aggregated based on the estimated quality of the corresponding instance. By doing so, we take advantage of all pieces of information while accounting for the reduced reliability of images impaired by multiple processing steps. To support large-scale evaluation, we introduce two datasets: AncesTree, an in-lab dataset of 136k images organized in stochastic degradation trees that simulate online reposting dynamics, and ReWIND, a real-world dataset of nearly 10k near-duplicate images collected from viral web content. Experiments on several state-of-the-art detectors show that our quality-aware fusion improves their performance consistently, with an average gain of around 8% in terms of balanced accuracy compared to plain average. Our results highlight the importance of jointly processing all the images available online to achieve reliable detection of AI-generated content in real-world applications. Code and data are publicly available at https://grip-unina.github.io/QuAD/
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。