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Novel color image steganalysis method based on RGB channel empirical modes to expose stego images with diverse payloads

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Abstract

With the rapid development of technology in the modern digital world, covert communication of secret information as a payload without instigating visible attention by using steganography emerged as a possible threat. Steganographic methods select either image contents like edges or random regions of image for hiding payload. Steganalysis methods generally concentrate on content-adaptive algorithms of grayscale images but only few works concentrate on color image steganalysis. To address this issue, a generalized steganalyzer that can identify suspicious content created using steganography methods in digital color images is a need of the hour. In this paper, a novel FroFeat feature extracted from decomposed components of three color channels using empirical mode decomposition process is proposed to augment the existing color rich model features to detect stego images created using five content—adaptive and eight non-content—adaptive steganography methods. These empirical mode decomposed components eliminate the image content including edges and pave way to model the subtle stego noise hidden inside stego images. The proposed method is validated by comparing the performance metrics with existing state-of-the-art steganalysis models. Based on the experimental results, the proposed method achieves an average of 0.484 decrease in detection error for low-volume payload detection compared to the existing methods. Also in this paper, mixed generic color image steganalysis is performed to showcase the generalization ability of the proposed steganalysis method.

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Data availability

The cover images dataset analyzed during the current study are available in the McGill Calibrated Color Image Database repository. [http://tabby.vision.mcgill.ca/html/browsedownload.html]

Code availability

Not applicable.

Availability of data and material

Not applicable.

Notes

  1. Code for CRMQ1 feature downloaded from http://dde.binghamton.edu/download/feature_extractors/.

  2. Dataset downloaded from http://tabby.vision.mcgill.ca/html/browsedownload.html.

  3. Code for steganographic algorithms downloaded from http://dde.binghamton.edu/download/stego_algorithms/.

  4. https://code.google.com/p/2pix-steganography/.

  5. http://diit.sourceforge.net/.

  6. https://invisible-secrets.en.uptodown.com/windows.

  7. https://silenteye.v1kings.io/download.html?i2.

  8. http://techgenix.com/stenographyresources/.

  9. http://wbstego.wbailer.com/.

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Funding

The work presented in this paper was funded by Directorate of Extramural Research & Intellectual Property Rights (ER & IPR), Defence Research Development Organization (DRDO), Ministry of Defence, Government of India under Grant No. ERIP/ER/201702007/M/01/1733. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied of Indian Government.

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Authors and Affiliations

  1. Centre for Image Processing and Pattern Recognition, Department of Electronics and Communication Engineering, MEPCO Schlenk Engineering College (Autonomous), Sivakasi, 626005, India

    E. Amrutha, S. Arivazhagan & W. Sylvia Lilly Jebarani

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  1. E. Amrutha
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  2. S. Arivazhagan
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  3. W. Sylvia Lilly Jebarani
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All authors contributed to the study conception and design, material preparation, data collection and analysis. All authors read and approved the final manuscript.

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Correspondence to E. Amrutha.

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Amrutha, E., Arivazhagan, S. & Jebarani, W.S.L. Novel color image steganalysis method based on RGB channel empirical modes to expose stego images with diverse payloads. Pattern Anal Applic 26, 239–253 (2023). https://doi.org/10.1007/s10044-022-01102-2

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