AI 中文总结
针对现有AI生成图像检测方法易被高信噪比语义组件主导的问题，本文提出基于局部差分信号的RippleNet框架，通过自适应捕捉像素级伪造痕迹，在多基准实验中取得了竞争力性能。
AI 中文摘要
AI生成内容的快速发展使得生成图像的可靠检测成为日益关键的挑战。现有检测方法在训练中往往被具有高信噪比（SNR）的语义显著组件主导，从而抑制了与底层生成机制相关、嵌入在低级统计结构中的更细微取证线索。从信息论角度，本文提出关键见解：在低级统计空间中进行有效检测，需要减轻语义组件的主导作用，同时强调并放大对低SNR伪造痕迹的响应。基于此见解，本文提出RippleNet，一种基于局部差分信号的AI生成图像检测框架。RippleNet自适应识别伪造敏感区域，并在局部邻域内构建多方向、多尺度差分表示，明确表征邻域统计中的异常模式。更重要的是，本文对注意力机制进行改进，使其在局部差分表示空间中运行，使模型能够在更精细的统计粒度上建立显式依赖关系。该设计有助于捕捉传统卷积或全图像补丁级注意力难以建模的像素级伪造痕迹。在多个公共基准及跨生成器评估设置下开展的大量实验表明，RippleNet始终取得具有竞争力的性能。
英文摘要
The rapid advancement of AI-generated content has made the reliable detection of generated images an increasingly critical challenge. Existing detection methods are often dominated during training by semantically salient components with high signal-to-noise ratios (SNRs), thereby suppressing subtler forensic cues associated with the underlying generation mechanisms and embedded in low-level statistical structures. From an information-theoretic perspective, we present a key insight: effective detection in the low-level statistical space requires mitigating the dominance of semantic components while emphasizing and amplifying responses to low-SNR forgery traces. Building on this insight, we propose RippleNet, an AI-generated image detection framework based on local differential signals. RippleNet adaptively identifies forgery-sensitive regions and constructs multi-directional, multi-scale differential representations within local neighborhoods, explicitly characterizing anomalous patterns in neighborhood statistics. More importantly, we refine the attention mechanism to operate within the local differential representation space, enabling the model to establish explicit dependencies at a finer statistical granularity. This design facilitates the capture of pixel-level forgery traces that are difficult to model using conventional convolutions or image-wide patch-level attention. Extensive experiments on multiple public benchmarks and under cross-generator evaluation settings demonstrate that RippleNet achieves consistently competitive performance.
