用条件似然方法实现跨领域的生成图像零样本检测
General and Domain-Specific Zero-shot Detection of Generated Images via Conditional Likelihood
- 基于真实图像条件的似然估计，动态适应不同图像领域
- 在通用数据集上达到当前最佳性能，在特定领域表现更优
- 适合需要快速部署、无需标注数据的生成图像检测场景
生成模型，尤其是基于扩散的方法，显著提升了合成图像的真实感。随着新生成模型不断涌现，检测生成图像仍是一个关键挑战。尽管已提出全监督和少样本方法，但维护更新数据集耗时且困难。因此，零样本方法近年来受到越来越多关注。我们发现现有零样本方法往往难以适应特定图像领域（如艺术图像），限制了其实用性。本文提出一种基于条件似然近似的新型零样本检测方法——CLIDE。该方法计算以真实图像为条件的似然值，实现对多样化图像领域的自适应。我们在大规模通用数据集上广泛评估了CLIDE，结果显示其在通用任务中达到最先进性能，并在特定领域任务中显著优于现有方法。这些结果证明了该方法的鲁棒性，也强调了面向广泛、领域感知的泛化能力对生成图像检测任务的重要性。代码已公开于 https://tinyurl.com/clide-detector。
原文摘要 · Abstract (English)
The rapid advancement of generative models, particularly diffusion-based methods, has significantly improved the realism of synthetic images. As new generative models continuously emerge, detecting generated images remains a critical challenge. While fully supervised, and few-shot methods have been proposed, maintaining an updated dataset is time-consuming and challenging. Consequently, zero-shot methods have gained increasing attention in recent years. We find that existing zero-shot methods often struggle to adapt to specific image domains, such as artistic images, limiting their real-world applicability. In this work, we introduce CLIDE, a novel zero-shot detection method based on conditional likelihood approximation. Our approach computes likelihoods conditioned on real images, enabling adaptation across diverse image domains. We extensively evaluate CLIDE, demonstrating SOTA performance on a large-scale general dataset and significantly outperform existing methods in domain-specific cases. These results demonstrate the robustness of our method and underscore the need of broad, domain-aware generalization for the AI-generated image detection task. Code is available at https://tinyurl.com/clide-detector.
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