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Improved Manipulation Detection with Convolutional Neural Network for JPEG Images

: Liu, Huajian; Steinebach, Martin; Schölei, Kathrin


Association for Computing Machinery -ACM-:
ARES 2019, 14th International Conference on Availability, Reliability and Security. Proceedings : Canterbury, CA, United Kingdom, August 26 - 29, 2019
New York: ACM, 2019
ISBN: 978-1-4503-7164-3
Art. 39, 6 S.
International Conference on Availability, Reliability and Security (ARES) <14, 2019, Canterbury>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Fraunhofer SIT ()
image forensics; manipulation detection; convolutional neural network; JPEG images

JPEG images are ubiquitously used in most real-world online and mobile applications, where uncompressed images are not available from the very beginning when digital images are generated by digital camera or smartphone. In this paper, an improved manipulation detection scheme for JPEG images with convolutional neural network is proposed which works better in practical scenarios. No uncompressed or lossless compressed images are used for network training and testing. All images are stored in JPEG format before and after any kind of manipulation. The proposed scheme is also able to detect manipulation even if the manipulated images are compressed with different JPEG quality factors from the training images. Experimental results demonstrate that the proposed scheme significantly outperforms the existing method under practical conditions of real-world applications.