学习连续源响应以实现可泛化的AI生成图像检测

中文摘要 AI 辅助

图像生成技术的进步使得合成图像越来越难以与真实照片区分，引发了对视觉媒体可信度的担忧。现有的AI生成图像检测器在域内数据上通常表现良好，但其鲁棒性和跨生成器泛化能力仍然有限。这些局限性通常归因于对捷径线索的过拟合。尽管许多方法试图抑制捷径学习，但大多数仍将二分类作为训练任务，而未重新考虑任务本身如何塑造学习到的表示。我们提出了CuRe，一个学习连续源响应的框架，从训练任务的角度重新审视真实性检测。CuRe将骨干网络适应重新表述为真实-生成混合比例的回归，提供更细粒度的监督，鼓励模型捕获超出二分类端点分离的真实性相关变化。我们进一步选择一个紧凑的源响应子空间，以抑制干扰变化并限制最终分类器对潜在捷径线索的访问。在十个公开基准上，CuRe实现了89.7%的平均平衡准确率，超过第二好的方法5.2个百分点。进一步的实验证明了在不同视觉骨干网络上的一致泛化提升以及对常见图像退化的强鲁棒性。代码可在以下网址获取：https://this https URL

英文摘要

Advances in image generation have made synthetic images increasingly difficult to distinguish from real photographs, raising concerns about the trustworthiness of visual media. Existing AI-generated image detectors often perform well on in-domain data, but their robustness and cross-generator generalization remain limited. These limitations are commonly attributed to overfitting to shortcut cues. Although many methods seek to suppress shortcut learning, most retain binary classification as the training task without reconsidering how the task itself shapes the learned representations. We introduce CuRe, a framework for learning Continuous Source Responses that revisits authenticity detection from the perspective of the training task. CuRe reformulates backbone adaptation as regression of real-generated mixing ratios, providing finer supervision that encourages the model to capture authenticity-related variation beyond binary endpoint separation. We further select a compact source-response subspace to suppress nuisance variation and limit the final classifier's access to potential shortcut cues. Across ten public benchmarks, CuRe achieves an average balanced accuracy of 89.7%, exceeding the second-best method by 5.2 percentage points. Further experiments demonstrate consistent generalization gains across visual backbones and strong robustness to common image degradations. Code is available at https://github.com/manic-cui/CuRe

发表机构

• Ant Group（蚂蚁集团）
• Huazhong University of Science and Technology（华中科技大学）
• Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
• Jilin University（吉林大学）

机构由 AI 辅助整理，请以论文原文为准。