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Steganalysis using learned denoising kernels

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Abstract

Steganalysis is the science for detecting steganographic traces in innocent-looking digital media like images, videos, etc. In recent literature, it has been observed that state-of-the-art image steganographic techniques such as S-UNIWARD, HUGO, WOW, etc. still remain undetected even with considerable embedding payload. Recently, the deep learning framework has been hugely successful in different computer vision applications like object detection, image classification, event detection, etc. Some recent deep learning-based works also show promising results for image steganalysis and have opened a new avenue for research. The current literature reveals that the steganalytic detector becomes more precise if trained on the residual error (embedding noise) domain. To get an accurate noise residual, it is required to predict the cover image precisely from the corresponding stego image. In this work, a denoising kernel has been learned to obtain a more precise noise residual. After that, a CNN based steganalytic detector is devised, which is trained using the noise residual to get a more precise detection. Experimental results show that the proposed scheme outperforms the state-of-the-art steganalysis schemes against the state-of-the-art steganographic approaches.

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Notes

  1. In order to find the suitable kernel size for denoising, the extensive experiment has been carried out with different filter sizes (e.g., 3 × 3, 5 × 5 and 7 × 7). The 5×5 filters are found to be suitable as denoising filters.

  2. SRNet model code is available at http://dde.binghmton.edu/download/

  3. SRNet trained weights, test data, and codes are available at https://drive.google.com/open?id=1MxObzvnkFSSGR4gcfcl_Esqp27kSk5TN

  4. http://tflearn.org/

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Acknowledgment

This work is supported by Ministry of Human Resource Development, Government of India.

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Authors and Affiliations

  1. Department of CSE, Indian Institute of Technology, Guwahati, India

    Brijesh Singh, Mohit Chhajed, Arijit Sur & Pinaki Mitra

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  1. Brijesh Singh
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  2. Mohit Chhajed
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Correspondence to Brijesh Singh.

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Singh, B., Chhajed, M., Sur, A. et al. Steganalysis using learned denoising kernels. Multimed Tools Appl 80, 4903–4917 (2021). https://doi.org/10.1007/s11042-020-09960-w

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  • DOI: https://doi.org/10.1007/s11042-020-09960-w

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