Abstract
When people tried to use existing image manipulation detection methods in real senses, some extreme limitations like lack of robustness against image recompression and resampling were found. In contrast, with the advancement high-quality images become too large to be loaded by neural network. Accordingly, the capability of learning manipulations-sensitive generalizable or even residual semantic features of compressed images becomes a key precondition for the practical value of algorithm. In this paper, we propose a convolutional neural network which uses error level analysis (ELA) to address the interest area and mask semantic information under different image compressions scenarios. We use that network to collect residual semantic features and use a discrete cosine transform (DCT) network to collect compression artifacts. A network which is termed MSEA-Net is realized to explore manipulation detection of recompressed images. The excellent results on NIST16, Columbia, CASIAv2 datasets and post-processed datasets demonstrate the capability of MSEA-Net for recompression and resampling manipulation detection.
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Xu, S., Ye, H. (2022). Image Tampering Localization Based on Two-Stream Weighted Fusion Features. In: Proceedings of 2022 10th China Conference on Command and Control. C2 2022. Lecture Notes in Electrical Engineering, vol 949. Springer, Singapore. https://doi.org/10.1007/978-981-19-6052-9_14
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DOI: https://doi.org/10.1007/978-981-19-6052-9_14
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