让AI生成图的真假判断可解释，32x32小图也能精准识别并说明原因。
VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systems
- 基于图像局部伪影定位与语义推理，实现小图真假判断
- 零样本下对32x32图检测准确率高，支持跨模型泛化
- 输出人类可读解释，适合需透明性的内容审核场景
AI生成内容（如GAN、扩散模型）的快速普及重塑了数字媒体生态，但真实图像与合成图像界限模糊，引发真实性与完整性担忧。现有检测方法多聚焦高分辨率图像分类，缺乏决策透明性，难以解释为何判定为伪造。本文提出VERITAS框架，不仅能精准识别32x32小尺寸图像是否由AI生成，还通过伪影定位与语义推理提供可读解释，揭示合成图像中的关键异常特征。该架构在零样本条件下实现清晰的检测依据，支持跨模型泛化。代码与提示词已开源：https://github.com/V-i-g-n-e-s-h-N/VERITAS。
原文摘要 · Abstract (English)
The widespread and rapid adoption of AI-generated content, created by models such as Generative Adversarial Networks (GANs) and Diffusion Models, has revolutionized the digital media landscape by allowing efficient and creative content generation. However, these models also blur the difference between real images and AI-generated synthetic images, raising concerns regarding content authenticity and integrity. While many existing solutions to detect fake images focus solely on classification and higher-resolution images, they often lack transparency in their decision-making, making it difficult for users to understand why an image is classified as fake. In this paper, we present VERITAS, a comprehensive framework that not only accurately detects whether a small (32x32) image is AI-generated but also explains why it was classified that way through artifact localization and semantic reasoning. VERITAS produces human-readable explanations that describe key artifacts in synthetic images. We show that this architecture offers clear explanations of the basis of zero-shot synthetic image detection tasks. Code and relevant prompts can be found at https://github.com/V-i-g-n-e-s-h-N/VERITAS .
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