Abstract
Image tampering forensics is performed by analyzing images to locate the tampered regions. However, most image tampering detection methods lack locational accuracy and are effective only for specific types of tampering. To address these problems, this chapter proposes a method that employs an encoder-decoder network structure with combined multiple feature encoding to segment tampered regions of an image from untampered regions. Three features, obtained using constrained convolution, steganalysis rich model filtering and common convolution, are combined. During the encoding stage, ring residual units are used to extract features. The combination of multiple features and the ring residual units makes the proposed method most suitable for image tampering detection. Channel attention with a soft threshold function is used to reinforce semantic information in the decoding stage. Experiments with three image forensic datasets, NIST16, COVERAGE and CASIA, demonstrate that the proposed method exhibits strong performance in terms of the F1 score and localization of tampered regions.
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Luo, Y., Liang, C., Zhang, S., Qin, S. (2022). A Combined Feature Encoding Network with Semantic Enhancement for Image Tampering Forensics. In: Peterson, G., Shenoi, S. (eds) Advances in Digital Forensics XVIII. DigitalForensics 2022. IFIP Advances in Information and Communication Technology, vol 653. Springer, Cham. https://doi.org/10.1007/978-3-031-10078-9_6
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DOI: https://doi.org/10.1007/978-3-031-10078-9_6
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