Abstract
The most popular challenges in steganalysis is to identify the characterstics, to discover the stego-images. In this, we derive a steganalysis measure using Gaussian distribution, for image modeling. By using Gaussian distribution model the distribution of DCT coefficients and quantify a ratio of two Fourier coefficients of the distribution of DCT coefficients [9]. This derive steganalysis measure is evaluated against three steganographic methods i.e. first one is LSB (Least Significant Bit), the second one is SSIS (Spread Spectrum Image Steganography), and the last one is Steg-Hide tool, which is based on graph theoretic approach. Classification of image features dataset is done by using different classification techniques such as SVM.
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Rani, A., Kumar, M., Goel, P. (2017). Image Modelling: A Feature Detection Approach for Steganalysis. In: Singh, M., Gupta, P., Tyagi, V., Sharma, A., Ören, T., Grosky, W. (eds) Advances in Computing and Data Sciences. ICACDS 2016. Communications in Computer and Information Science, vol 721. Springer, Singapore. https://doi.org/10.1007/978-981-10-5427-3_15
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DOI: https://doi.org/10.1007/978-981-10-5427-3_15
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