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A semi-feature learning approach for tampered region localization across multi-format images

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Abstract

In multimedia security, it is an important task to localize the tampered image regions. In this work, deep learning is used to solve this problem and the approach can be applied to multi-format images. Concretely, we use Stack Autoencoder to obtain the tampered image block features so that the forgery can be identified in a semi-automatic manner. Contextual information of image block is further integrated to improve the localization accuracy. The approach is tested on a benchmark dataset, with a 92.84% localization accuracy and a 0.9375 Area Under Curve (AUC) score. Compared to the state-of-the-art solutions for multi-format images, our solution has an over 40% AUC improvement and 5.7 times F1 improvement. The results also out-perform several approaches which are designed specifically for JPEG images by 41.12%∼63.08% in AUC and with a 4∼8 times better F1.

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Acknowledgments

This material is based on research work supported by the Singapore National Research Foundation under NCR Award No. NRF2014NCR-NCR001-034.

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Authors and Affiliations

  1. Cyber Security Cluster, Institute for Infocomm Research, Singapore, Singapore

    Ying Zhang & Vrizlynn L. L. Thing

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  1. Ying Zhang
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Correspondence to Ying Zhang.

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Zhang, Y., Thing, V.L.L. A semi-feature learning approach for tampered region localization across multi-format images. Multimed Tools Appl 77, 25027–25052 (2018). https://doi.org/10.1007/s11042-018-5756-4

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  • DOI: https://doi.org/10.1007/s11042-018-5756-4

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