Open Access

ARTICLE

A New Method for Image Tamper Detection Based on an Improved U-Net

by Jie Zhang, Jianxun Zhang^*, Bowen Li, Jie Cao, Yifan Guo

Department of Computer Science and Engineering, Chongqing University of Technology, Chongqing, 40005, China

* Corresponding Author: Jianxun Zhang. Email:

(This article belongs to the Special Issue: Artificial Intelligence Algorithm for Industrial Operation Application)

Intelligent Automation & Soft Computing 2023, 37(3), 2883-2895. https://doi.org/10.32604/iasc.2023.039805

Received 17 February 2023; Accepted 28 April 2023; Issue published 11 September 2023

Abstract

With the improvement of image editing technology, the threshold of image tampering technology decreases, which leads to a decrease in the authenticity of image content. This has also driven research on image forgery detection techniques. In this paper, a U-Net with multiple sensory field feature extraction (MSCU-Net) for image forgery detection is proposed. The proposed MSCU-Net is an end-to-end image essential attribute segmentation network that can perform image forgery detection without any pre-processing or post-processing. MSCU-Net replaces the single-scale convolution module in the original network with an improved multiple perceptual field convolution module so that the decoder can synthesize the features of different perceptual fields use residual propagation and residual feedback to recall the input feature information and consolidate the input feature information to make the difference in image attributes between the untampered and tampered regions more obvious, and introduce the channel coordinate confusion attention mechanism (CCCA) in skip-connection to further improve the segmentation accuracy of the network. In this paper, extensive experiments are conducted on various mainstream datasets, and the results verify the effectiveness of the proposed method, which outperforms the state-of-the-art image forgery detection methods.

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