Noise-aware cross attention for image manipulation localization

• A Gated Noise Extractor that dynamically captures noise features from multiple strategies. • Dual-granularity contrastive learning for more discriminative noise extraction. • Noise-domain guided fusion module t • reduce interference from irrelevant in- formation. • An efficient model with low parameter count and computational complexity. Modern image manipulation techniques have achieved visual realism that often deceives the human eye and semantic-based detectors. However, manipulation operations typically disturb the intrinsic statistical properties of images. Unlike high-level semantic content, which remains visually consistent, such disturbances manifest as anomalies in noise characteristics, including inconsistencies in sensor pattern noise, distinct high-frequency residuals, and unnatural frequency-domain artifacts introduced by resampling or synthesis. These subtle forensic cues provide more reliable evidence for manipulation localization but are often suppressed by standard RGB-domain feature extractors. Existing IML methods often rely on a single noise feature extraction strategy or treat all tampering techniques uniformly, leading to two major limitations, incomplete noise characterization and insufficient tampering-type awareness . We propose a Noise-aware Contrastive localization Network (NC-Net), which introduces two key modules. Firstly, a Gated Noise Extractor that captures mixed noise-domain patterns using a gated network combining features derived from BayarConv and Discrete Wavelet Transform (DWT) operations. This extractor is further enhanced by a dual-granularity contrastive learning strategy, which models distributional discrepancies both within images (between manipulated and authentic regions) and across images (among different manipulation types). Secondly, a Multi-Scale Fusion Module that adaptively integrates noise-domain and RGB-domain semantic features via a cross-domain attention mechanism and a top-down feature pyramid. A lightweight decoder then produces the final localization map with high precision. NC-Net enables end-to-end joint optimization of the noise extraction and RGB branches, achieving state-of-the-art performance with competitive computational overhead. Extensive experiments demonstrate its superiority over existing methods. Source code is available at https://github.com/HIT-liar/NC-Net .