Recent generative models show impressive performance in generating photographic images. Humans can hardly distinguish such incredibly realistic-looking AI-generated images from real ones. AI-generated images may lead to ubiquitous disinformation dissemination. Therefore, it is of utmost urgency to develop a detector to identify AI-generated images. Most existing detectors suffer from sharp performance drops over unseen generative models. In this paper, we propose a novel AI-generated image detector capable of identifying fake images created by a wide range of generative models. Our approach leverages the inter-pixel correlation contrast between rich and poor texture regions within an image. Pixels in rich texture regions exhibit more significant fluctuations than those in poor texture regions. This discrepancy reflects that the entropy of rich texture regions is larger than that of poor ones. Consequently, synthesizing realistic rich texture regions proves to be more challenging for existing generative models. Based on this principle, we divide an image into multiple patches and reconstruct them into two images, comprising rich-texture and poor-texture patches respectively. Subsequently, we extract the inter-pixel correlation discrepancy feature between rich and poor texture regions. This feature serves as a universal fingerprint used for AI-generated image forensics across different generative models. In addition, we build a comprehensive AI-generated image detection benchmark, which includes 16 kinds of prevalent generative models, to evaluate the effectiveness of existing baselines and our approach. Our benchmark provides a leaderboard for follow-up studies. Extensive experimental results show that our approach outperforms state-of-the-art baselines by a significant margin. Our project: https://fdmas.github.io/AIGCDetect/

通过利用图像中富质地和贫质地纹理区域之间的像素间关联差异特征，我们提出了一种能够识别广泛范围生成模型生成的假图像的新型AI生成图像检测器，并构建了一个综合的AI生成图像检测基准来评估现有基线与我们的方法的有效性。

富贫纹理对比：一种简单而有效的AI生成图像检测方法