Multitask Image Splicing Tampering Detection Based on Attention Mechanism

Pingping Zeng, Lianhui Tong, Yaru Liang, Nanrun Zhou () and Jianhua Wu ()
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Pingping Zeng: College of Science and Technology, Nanchang University, Jiujiang 332020, China
Lianhui Tong: School of Information Engineering, Nanchang University, Nanchang 330031, China
Yaru Liang: School of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Nanrun Zhou: School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
Jianhua Wu: School of Information Engineering, Nanchang University, Nanchang 330031, China

Mathematics, 2022, vol. 10, issue 20, 1-13

Abstract: In today’s modern communication society, the authenticity of digital media has never been of such importance as it is now. In this aspect, the reliability of digital images is of paramount importance because images can be easily manipulated by means of sophisticated software, such as Photoshop. Splicing tampering is a commonly used photographic manipulation for modifying images. Detecting splicing tampering remains a challenging task in the area of image forensics. A new multitask model based on attention mechanism, densely connected network, Atrous Spatial Pyramid Pooling (ASPP) and U-Net for locating splicing tampering in an image, AttDAU-Net, was proposed. The proposed AttDAU-Net is basically a U-Net that incorporates the spatial rich model filtering, an attention mechanism, an ASPP module and a multitask learning framework, in order to capture more multi-scale information while enlarging the receptive field and improving the detection precision of image splicing tampering. The experimental results on the datasets of CASIA1 and CASIA2 showed promising performance metrics for the proposed model ( F 1 -scores of 0.7736 and 0.6937, respectively), which were better than other state-of-the-art methods for comparison, demonstrating the feasibility and effectiveness of the proposed AttDAU-Net in locating image splicing tampering.

Keywords: image authenticity; image forgery; detection of splicing tampering; attention mechanism; multitask learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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