构建多维度图像篡改检测基准，助力识别生成式AI伪造内容。
Multi-axis Analysis of Image Manipulation Localization
- 提出AUDITS基准，覆盖域偏移、质量、类型、尺寸四类分析轴。
- 包含超53万张用户与新闻照片，支持扩散模型修复等多样化篡改。
- 揭示现有检测方法在跨域场景下的脆弱性，推动更鲁棒的检测研究。
先进的图像编辑软件使得高度逼真的图像篡改变得容易，近年来生成式AI的发展进一步降低了这一门槛。尽管这些篡改通常无害，但可能传播虚假信息、制造错误叙事，并影响公众对重要议题的看法。然而，针对不同视觉领域中先进篡改的检测研究仍有限。为此，我们提出了分析域偏移、质量、类型和尺寸（AUDITS）的综合基准，用于系统研究图像篡改检测的多个分析维度。AUDITS包含来自两类来源（用户照片与新闻照片）超过53万张图像，涵盖基于扩散模型的最新修复技术，涉及多种篡改类型和尺寸。我们在不同域偏移条件下进行实验，评估现有图像篡改检测方法的鲁棒性。本工作旨在通过新见解推动该领域研究，促进更可靠、可泛化的检测方法发展。
原文摘要 · Abstract (English)
Advanced image editing software enables easy creation of highly convincing image manipulations, which has been made even more accessible in recent years due to advances in generative AI. Manipulated images, while often harmless, could spread misinformation, create false narratives, and influence people's opinions on important issues. Despite this growing threat, there is limited research on detecting advanced manipulations across different visual domains. Thus, we introduce Analysis Under Domain-shifts, qualIty, Type, and Size (AUDITS), a comprehensive benchmark designed for studying axes of analysis in image manipulation detection. AUDITS comprises over 530K images from two distinct sources (user and news photos). We curate our dataset to support analysis across multiple axes using recent diffusion-based inpaintings, spanning a diverse range of manipulation types and sizes. We conduct experiments under different types of domain shift to evaluate robustness of existing image manipulation detection methods. Our goal is to drive further research in this area by offering new insights that would help develop more reliable and generalizable image manipulation detection methods.
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