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PSW statistical LSB image steganalysis

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Abstract

Steganography is the art and science of producing covert communications by concealing secret messages in apparently innocent media, while steganalysis is the art and science of detecting the existence of these. This manuscript proposes a novel blind statistical steganalysis technique to detect Least Significant Bit (LSB) flipping image steganography. It shows that the technique has a number of major advantages. First, a novel method of pixel color correlativity analysis in Pixel Similarity Weight (PSW). Second, filtering out image pixels according to their statistically detected suspiciousness, thereby excluding neutral pixels from the steganalysis process. Third, ranking suspicious pixels according to their statistically detected suspiciousness and determining the influence of such pixels based on the level of detected anomalies. Fourth, the capability to classify and analyze pixels in three pixel classes of flat, smooth and edgy, thereby enhancing the sensitivity of the steganalysis. Fifth, achieving an extremely high efficiency level of 98.049% in detecting 0.25bpp stego images with only a single dimension analysis.

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Acknowledgements

This work is a part of wider research supported by the Universiti Teknologi Malaysia.

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Authors and Affiliations

  1. Advanced Informatics School, International Campus, Universiti Teknologi Malaysia, Jalan Semarah, 45000, Kuala Lumpur, Malaysia

    Saman Shojae Chaeikar, Mazdak Zamani & Azizah Bt Abdul Manaf

  2. International Islamic University Malaysia, Kuala Lumpur, Selangor, Malaysia

    Akram M. Zeki

Authors
  1. Saman Shojae Chaeikar
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  2. Mazdak Zamani
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  3. Azizah Bt Abdul Manaf
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  4. Akram M. Zeki
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Correspondence to Saman Shojae Chaeikar.

Appendices

Appendix A – Pixel classification sample

After an initial PSW analysis of the image pixels, neutral pixels are excluded from the subsequent steganalysis process and suspicious pixels are categorized into three pixel classes of flat, smooth and edgy. Figure 12 below illustrates the performance of the PSW steganalysis in doing this.

Fig. 12
figure 12

An example of application of PSW steganalysis technique to classify suspicious image pixels into flat, smooth and edgy pixel classes

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Appendix B – Pixel reference profiles

Pixel reference profiles are a summary of the purified, classified and prioritized PSW analysis results of the SVM training database. The pixel reference profile with the highest matching score with the analyzed image helps to determine the results of the steganalysis in detecting embedded messages and estimating message length.

PSW steganalysis uses three pixel reference profiles: cover, 0.125bpp and 0.25bpp. The cover pixel reference profile was constructed from the PSW analysis results on cover images in the SVM training database, while the 0.125bpp and 0.25bpp pixel reference profiles were created from the PSW analysis results on 0.125bpp and 0.25bpp stego images respectively in the SVM training database. Tables 10, 11 and 12 present the cover, 0.125bpp and 0.25bpp pixel reference profiles respectively. In these tables, SD stands for standard deviation, S stands for achieved matching score, and x is the PSW value.

Table 10 Cover pixel reference profile
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Table 11 0.125bpp pixel reference profile
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Table 12 0.25bpp pixel reference profile
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Shojae Chaeikar, S., Zamani, M., Abdul Manaf, A.B. et al. PSW statistical LSB image steganalysis. Multimed Tools Appl 77, 805–835 (2018). https://doi.org/10.1007/s11042-016-4273-6

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