@Article{iasc.2023.039805,
AUTHOR = {Jie Zhang, Jianxun Zhang, Bowen Li, Jie Cao, Yifan Guo},
TITLE = {A New Method for Image Tamper Detection Based on an Improved U-Net},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {37},
YEAR = {2023},
NUMBER = {3},
PAGES = {2883--2895},
URL = {http://www.techscience.com/iasc/v37n3/54132},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2023.039805}
}