标题： 感知分类器：使用感知特征检测生成图像 显示英文标题

标题： Perceptual Classifiers: Detecting Generative Images using Perceptual Features

摘要： 图像质量评估（IQA）模型被用于许多实际的图像和视频处理流程中，以减少存储需求，降低传输成本，并提升数百万观众的体验质量（QoE）。 这些模型对各种图像失真都很敏感，并能准确预测人类观察者评判的图像质量。 生成模型的最新进展导致互联网上出现了大量“GenAI”内容。 现有的检测GenAI内容的方法在未见过的生成模型图像上的泛化性能有了显著提高。 在此，我们利用现有IQA模型的能力，这些模型能够有效捕捉真实图像在带通统计空间中的流形，以区分真实图像和AI生成的图像。 我们研究了这些感知分类器在GenAI图像检测任务中的泛化能力，并评估了它们对各种图像退化的鲁棒性。 我们的结果表明，在IQA模型特征空间上训练的两层网络在检测生成模型中的假图像方面表现出最先进的性能，同时在图像退化方面保持了显著的鲁棒性。 显示英文摘要

摘要： Image Quality Assessment (IQA) models are employed in many practical image and video processing pipelines to reduce storage, minimize transmission costs, and improve the Quality of Experience (QoE) of millions of viewers. These models are sensitive to a diverse range of image distortions and can accurately predict image quality as judged by human viewers. Recent advancements in generative models have resulted in a significant influx of "GenAI" content on the internet. Existing methods for detecting GenAI content have progressed significantly with improved generalization performance on images from unseen generative models. Here, we leverage the capabilities of existing IQA models, which effectively capture the manifold of real images within a bandpass statistical space, to distinguish between real and AI-generated images. We investigate the generalization ability of these perceptual classifiers to the task of GenAI image detection and evaluate their robustness against various image degradations. Our results show that a two-layer network trained on the feature space of IQA models demonstrates state-of-the-art performance in detecting fake images across generative models, while maintaining significant robustness against image degradations.