ReAlign：通过推理对齐表征实现可泛化的图像伪造检测
ReAlign: Generalizable Image Forgery Detection via Reasoning-Aligned Representation
把LLM生成的推理文本当作可蒸馏的语义监督信号，通过图文对比对齐将其迁移到轻量检测器中，为视觉取证任务提供了一条「用推理表征提升泛化性」的可复用路径，可直接用于AIGI检测与跨生成器泛化评测。
原文依据 distills high-quality reasoning texts generated by a GRPO-optimized LLM into a lightweight AIGI detector via contrastive learning
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论AI生成图像（AIGI）的兴起对数字真实性构成日益严峻的挑战，由此产生了对高效、可泛化的图像伪造检测系统的需求。现有方法无论是否基于大语言模型（LLM），都表现出各自的优势与局限。非LLM模型擅长高效的低层伪影检测，但往往缺乏语义理解；相反，基于LLM的方法具备强大的语义推理能力与可解释性，但计算开销大，且对细微视觉伪影不够敏感。此外，解释性推理文本对伪造检测性能的真实贡献仍不明确。在本工作中，我们探究了LLM生成的推理文本的内在价值与潜力，将其视为泛化性与语义错误敏感性的来源。基于这些发现，我们提出ReAlign，一种新颖的框架，通过对比学习将由GRPO优化的LLM生成的高质量推理文本蒸馏到轻量级AIGI检测器中。ReAlign有效继承了推理文本表征的泛化能力与语义敏感性，同时保持高效轻量、便于部署。此外，ReAlign采用量身定制的联合优化策略，将用于图文对齐的对比损失与用于准确伪造判别的分类损失相结合。在AIGCDetectBenchmark、AIGI-Holmes以及我们新构建的UltraSynth-10k上的实验结果表明，ReAlign在准确率与泛化性上均持续优于现有最优检测器，尤其是在面对现代生成模型产生的复杂高保真伪造时。
英文原文摘要 1712 字符
The rise of AI-generated images (AIGIs) poses growing challenges for digital authenticity, prompting the need for efficient, generalizable image forgery detection systems. Existing methods, whether non-LLM-based or LLM-based, exhibit distinct advantages and limitations. While non-LLM-based models offer efficient low-level artifact detection, they often lack semantic understanding. Conversely, LLM-based methods provide strong semantic reasoning and explainability but are computationally intensive and less sensitive to subtle visual artifacts. Moreover, the true contribution of explanatory reasoning texts to forgery detection performance remains unclear. In this work, we investigate the intrinsic value and potential of LLM-generated reasoning texts, considering it a source of generalization and semantic-error sensitivity. Based on these findings, we propose ReAlign, a novel framework that distills high-quality reasoning texts generated by a GRPO-optimized LLM into a lightweight AIGI detector via contrastive learning. ReAlign effectively inherits the generalization ability and semantic sensitivity capability of reasoning textual representations, while remaining efficient and lightweight for deployment. Moreover, ReAlign adopts a tailored joint optimization strategy that integrates contrastive loss for image-text alignment and classification loss for accurate forgery discrimination. Experimental results on AIGCDetectBenchmark, AIGI-Holmes, and our newly constructed UltraSynth-10k demonstrate that ReAlign consistently outperforms existing state-of-the-art detectors in both accuracy and generalization, particularly when facing complex, high-fidelity forgeries from modern generative models.