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Math Biosci Eng. 2019 May 31;16(5):5022-5040. doi: 10.3934/mbe.2019253.

Resampling detection of recompressed images via dual-stream convolutional neural network.

Author information

1
School of Computer Science and Cybersecurity, Communication University of China, Beijing 100024, China.
2
Key Laboratory of Convergent Media and Intelligent Technology (Communication University of China), Ministry of Education, China.
3
School of Data Science and Media Intelligence, Communication University of China, Beijing 100024, China.

Abstract

Resampling detection plays an important role in identifying image tampering, such as image splicing. Currently, the resampling detection is still difficult in recompressed images, which are yielded by applying resampling followed by post-JPEG compression to primary JPEG images. Except for the scenario of low quality primary compression, it remains rather challenging due to the widespread use of middle/high quality compression in imaging devices. In this paper, we propose a new convolution neural network (CNN) method to learn the resampling trace features directly from the recompressed images. To this end, a noise extraction layer based on low-order high pass filters is deployed to yield the image residual domain, which is more beneficial to extract manipulation trace features. A dual-stream CNN is presented to capture the resampling trails along different directions, where the horizontal and vertical network streams are interleaved and concatenated. Lastly, the learned features are fed into Sigmoid/Softmax layer, which acts as a binary/multiple classifier for achieving the blind detection and parameter estimation of resampling, respectively. Extensive experimental results demonstrate that our proposed method could detect resampling effectively in recompressed images and outperform the state-of-the-art detectors.

KEYWORDS:

convolutional neural network; image forensics; interleaved stream; recompressed image; resampling detection

PMID:
31499702
DOI:
10.3934/mbe.2019253
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