Search papers, labs, and topics across Lattice.
This paper introduces RippleNet, a novel framework for detecting AI-generated images by focusing on local differential signals rather than relying solely on high-SNR semantic components. By emphasizing low-SNR forgery traces and refining the attention mechanism to operate within local differential representations, RippleNet effectively captures subtle pixel-level anomalies that traditional methods struggle to identify. Experimental results across various benchmarks indicate that RippleNet consistently outperforms existing detection techniques, highlighting its robustness in distinguishing generated content from real images.
RippleNet reveals that focusing on low-SNR forgery traces can significantly enhance the detection of AI-generated images, outperforming traditional methods.
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.
