Structured Local Differential Modeling for AI-Generated Image Detection

2026-08-13

AI Summary

Existing methods for detecting AI-generated images are often hindered by high-level semantic interference and struggle to capture subtle, low signal-to-noise ratio artifacts indicative of forgery. To address this limitation, this work proposes RippleNet, a novel framework that constructs local differential representations across multi-directional and multi-scale neighborhoods and introduces an innovative attention mechanism operating directly in the differential space to explicitly model pixel-level statistical anomalies. This approach overcomes the limitations of conventional convolutional operations and global attention mechanisms in capturing fine-grained forensic traces. Extensive experiments demonstrate that RippleNet achieves competitive detection performance across multiple public benchmarks and generalizes effectively under cross-generator settings, confirming its robustness and efficacy.

Abstract

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.

Problem

• AI-generated image detection
• low-level statistical structures
• forgery traces
• signal-to-noise ratio
• local differential modeling

Innovation

• local differential modeling
• low-SNR forgery detection
• statistical anomaly characterization
• adaptive attention mechanism
• AI-generated image detection