让AI检测假图时能说人话，提升透明度与可信度。
AI-Generated Images: What Humans and Machines See When They Look at the Same Image
- 用16种可解释AI方法生成视觉解释，增强检测结果可读性。
- 基于100人调查验证，优化解释清晰度与人类理解匹配度。
- 针对文本生成图像的假图，构建可信赖的检测与解释框架。
生成式AI在在线虚假信息传播中的滥用凸显了开发透明、可解释检测系统的重要性。本文构建了一套多种架构与微调策略的检测模型，基于大规模逼真假图数据集AIText2Image训练，并评估其在主流文生图AI生成器上的表现。将16种可解释AI（XAI）方法集成到检测框架中，通过一项新方法对视觉解释进行系统优化与评估，该方法以人类对假图的理解为核心，收集了100名参与者提供的文本与视觉反馈。框架揭示了假图检测中的视觉-语言线索，并从人类视角衡量了XAI输出的清晰度与偏好一致性，为可解释检测提供了实证支持。
原文摘要 · Abstract (English)
The misuse of generative AI in online disinformation campaigns highlights the urgent need for transparent and explainable detection systems. In this work, we investigate how detectors for AI-generated images can be more effective in providing human-understandable explanations for their predictions. To this end, we develop a suite of detectors with various architectures and fine-tuning strategies, trained on our large-scale photorealistic fake image dataset, AIText2Image, and assess their performance on state-of-the-art text-to-image AI generators. We integrate 16 different explainable AI (XAI) methods into our detection framework, and the visual explanations are comprehensively refined and evaluated through a novel approach that prioritizes human understanding of AI-generated images, using both textual and visual responses collected from a survey of 100 participants. This framework offers insights into visual-language cues in fake image detection and into the clarity of XAI methods from a human perspective, measuring the alignment of XAI outputs with human preferences.
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