开放集AIGC检测的测试时课程

中文摘要 AI 辅助

部署在开放世界环境中的AI生成图像检测器，随着新的、更强的生成模型不断出现，不可避免地会面临分布偏移问题。尽管现有方法通过更好的表示或训练数据构建提升了跨生成器的泛化能力，但它们通常遵循静态的“一次训练、部署”范式，部署后无法适应。本研究从测试时适应的角度研究开放集AIGC图像检测，提出Test-Time Curriculum（TTC，测试时课程），这是一种简单且与模型无关的框架，通过基于课程的自监督学习在未标记的测试数据上适配检测器。TTC从高度可靠的伪标记样本开始，逐步纳入更难但有信息的样本，同时执行类平衡选择以减少生成器偏移下的有偏更新。为进一步提升伪标签质量，我们引入Cross-Scale Pseudo-Label Refinement（跨尺度伪标签细化），该方法聚合多个分辨率的互补证据以实现更可靠的适配，并在推理时应用noisy-or融合以强化最终预测。此外，我们构建了AIGCGuard，这是一个新的基准，包含3100张代表性真实图像和来自40种最先进的开源及专有文本到图像模型的124000张生成图像。在五个基准上的大量实验表明，TTC在各种未见生成器偏移下显著提升了整体检测性能，为开放集生成图像检测建立了实用且有效的测试时适应框架。

英文摘要

AI-generated image detectors deployed in open-world environments inevitably face distribution shifts as new and stronger generative models continue to emerge. Although existing methods improve cross-generator generalization through better representations or training data construction, they typically follow a static train-once-and-deploy paradigm and cannot adapt after deployment. In this work, we study open-set AIGC image detection from a test-time adaptation perspective. We propose Test-Time Curriculum (TTC), a simple and model-agnostic framework that adapts a detector on unlabeled test data through curriculum-based self-training. TTC starts from highly reliable pseudo-labeled samples and progressively incorporates harder yet informative cases, while enforcing class-balanced selection to reduce biased updates under generator shift. To further improve pseudo-label quality, we introduce Cross-Scale Pseudo-Label Refinement, which aggregates complementary evidence across multiple resolutions for more reliable adaptation, and applies noisy-or fusion at inference to strengthen final predictions. In addition, we construct AIGCGuard, a new benchmark containing 3,100 representative real images and 124,000 generated images from 40 of the most advanced open-source and proprietary text-to-image models. Extensive experiments on five benchmarks show that TTC substantially improves overall detection performance under diverse unseen-generator shifts, establishing a practical and effective test-time adaptation framework for open-set generated image detection.

发表机构

• Baidu Inc.（百度公司）

机构由 AI 辅助整理，请以论文原文为准。