Multiview image tampering detection and localization in real scene based on spatial-channel attention

Hanquan Liu; Shangping Zhong; Kaizhi Chen

doi:10.1117/12.3021612

25 March 2024 Multiview image tampering detection and localization in real scene based on spatial-channel attention

Hanquan Liu, Shangping Zhong, Kaizhi Chen

Proceedings Volume 13089, Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023); 130891U (2024) https://doi.org/10.1117/12.3021612
Event: Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023), 2023, Suzhou, China

Abstract

To cope with the threat of image content tampering in real scenes, this paper develops a multi-view spatial-channel attention network (MSCA-Net), which can use multi-view features and multi-scale features to detect whether an image has been tampered with and predict tampered regions. By introducing the frequency domain view of the image, the model can use the noise distribution around the tampered region to learn semantically independent features and detect subtle tampering traces that are difficult to detect in the RGB domain. Secondly, a new Efficient Spatial-Channel Attention Module (ESCM) is proposed to capture the correlation between different channels and between global pixels. MSCA-Net improves the localization performance of tampered regions on real-scene images by generating segmentation masks step by step at multiple scales through a progressive guidance mechanism. MSCA-Net runs very fast and is capable of processing 1080P resolution images at 40FPS+. Extensive experimental results demonstrate the promising performance of MSCA-Net on both image-level and pixel-level tampering detection tasks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hanquan Liu, Shangping Zhong, and Kaizhi Chen "Multiview image tampering detection and localization in real scene based on spatial-channel attention", Proc. SPIE 13089, Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023), 130891U (25 March 2024); https://doi.org/10.1117/12.3021612

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KEYWORDS

RGB color model

Object detection

Image segmentation

Data modeling

Image enhancement

Feature extraction

Performance modeling

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