• 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 .

Scene Text Editing (STE) involves replacing text in a scene image with new target text while preserving both the original text style and background texture. Existing methods suffer from two major challenges: inconsistency and length-insensitivity. They often fail to maintain coherence between the edited local patch and the surrounding area, and they struggle to handle significant differences in text length before and after editing. To tackle these challenges, we propose an end-to-end framework called Global-Local Aware Scene Text Editing (GLASTE), which simultaneously incorporates high-level global contextual information along with delicate local features. Specifically, we design a global-local combination structure, joint global and local losses, and enhance text image features to ensure consistency in text style within local patches while maintaining harmony between local and global areas. Additionally, we express the text style as a vector independent of the image size, which can be transferred to target text images of various sizes. We use an affine fusion to fill target text images into the editing patch while maintaining their aspect ratio unchanged. Extensive experiments on real-world datasets validate that our GLASTE model outperforms previous methods in both quantitative metrics and qualitative results and effectively mitigates the two challenges.

We propose and formulate the task of cross-language scene text editing, modifying the text content of a scene image into new text in another language, while preserving the scene text style and background texture. The key challenges of this task lie in the difficulty in distinguishing text and background, great distribution differences among languages, and the lack of fine-labeled real-world data. To tackle these problems, we propose a novel network named Cross-LAnguage Scene Text Editing (CLASTE), which is capable of separating the foreground text and background, as well as further decomposing the content and style of the foreground text. Our model can be trained in a self-supervised training manner on the unlabeled and multi-language data in real-world scenarios, where the source images serve as both input and ground truth. Experimental results on the Chinese-English cross-language dataset show that our proposed model can generate realistic text images, specifically, modifying English to Chinese and vice versa. Furthermore, our method is universal and can be extended to other languages such as Arabic, Korean, Japanese, Hindi, Bengali, and so on.