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Arbitrary-Sized JPEG Steganalysis Based on Fully Convolutional Network

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Digital Forensics and Watermarking (IWDW 2021)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 13180))

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Abstract

Steganography detectors based on convolutional neural networks have achieved significant performance in recently years. Most existing networks, however, only focus on the detection performance of fixed-size images, and the sizes of training and testing images are usually 256 \(\times \) 256 and 512 \(\times \) 512. It can not meet the practical requirement of detecting arbitrary-sized steganographic images. Furthermore, there are few efficient methods for arbitrary-sized JPEG steganalysis. In this paper, we proposed a novel end-to-end steganalyzer based on fully convolutional network to address this issue. The characteristic of only containing convolutional layers allows the network to train and test arbitrary-sized images. In addition, the U-shaped network design can make element-wise classification, which provides state-of-the-art detection accuracy for both fixed and arbitrary size. The experimental results on standard image sources BOSSBase 1.01 and ALASKA #2 show that the proposed network can achieves superior performance.

This work was supported by NSFC under 61972390, U1736214, 61872356 and 61902391, and National Key Technology Research and Development Program under 2019QY0701.

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Authors and Affiliations

  1. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Ante Su, Xianfeng Zhao & Xiaolei He

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, 100049, China

    Ante Su, Xianfeng Zhao & Xiaolei He

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  1. Ante Su
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  2. Xianfeng Zhao
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  3. Xiaolei He
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Correspondence to Xianfeng Zhao .

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Editors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Xianfeng Zhao

  2. University of Florence, Florence, Italy

    Alessandro Piva

  3. Universidade de Vigo, Vigo, Spain

    Pedro Comesaña-Alfaro

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Su, A., Zhao, X., He, X. (2022). Arbitrary-Sized JPEG Steganalysis Based on Fully Convolutional Network. In: Zhao, X., Piva, A., Comesaña-Alfaro, P. (eds) Digital Forensics and Watermarking. IWDW 2021. Lecture Notes in Computer Science(), vol 13180. Springer, Cham. https://doi.org/10.1007/978-3-030-95398-0_14

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  • DOI: https://doi.org/10.1007/978-3-030-95398-0_14

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  • Online ISBN: 978-3-030-95398-0

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