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ABSTRACT

This paper presents a novel steganalysis technique to attack quantization index modulation (QIM) steganography. Our method is based on the observation that QIM embedding disturbs neighborhood correlation in the transform domain. We estimate the probability density function (pdf) of this statistical change in a systematic manner using a kernel density estimate (KDE) method. The estimated parametric density model is then used for stego message detection. The impact of the choice of kernels on the estimated density is investigated experimentally. Simulation results evaluated on a large dataset of 6000 quantized images indicate that the proposed method is reliable. The impact of the choice of message embedding parameters on the accuracy of the steganalysis detection is also evaluated. Simulation results show that the proposed method can distinguish between the quantized-cover and the QIM-stego with low false alarm rates (i.e. P_fn≤0.03 and P_fp≤0.19). We demonstrate that the proposed steganalysis scheme can successfully attack steganographic tools like Jsteg and JP Hide and Seek as well.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.m Miscellaneous

General Terms:
Security

Keywords:
gamma density, kernel density estimation, mode, quantization index modulation, skewness, steganalysis, steganography