AI-Generated Images: What Humans and Machines See When They Look at the Same Image

• URL: http://arxiv.org/abs/2605.06143v1
• Date: Thu, 07 May 2026 12:40:13 GMT
• Title: AI-Generated Images: What Humans and Machines See When They Look at the Same Image
• Abstract summary: misuse of generative AI in online disinformation campaigns highlights need for transparent and explainable detection systems.<n>We develop a suite of detectors with various architectures and fine-tuning strategies.<n>We assess their performance on state-of-the-art text-to-image AI generators.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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