Information theoretic steganalysis of processed image LSB steganography

The preprocessing of a digital image affects the statistical properties of image pixels and steganographic security. Modification of digital media is quite a trivial task nowadays, and it is required intentionally for various reasons. Therefore, it is significant to discuss the usage of the preprocessed image in steganography and its impact on the security of embedded secret information. In this work, we attempt to estimate the impact of image operations, namely scaling, cropping, and contrast enhancement over LSB steganography. We first attack the original image dataset to produce a cover image dataset, then we apply steganography methods and measure their detection accuracy concerning the original image dataset. Steganalysis is conducted by utilizing the higher-order Markov features and newly introduced Joint Entropy-based features with SVM and ensemble classifiers. Experimental results show that the detection rate of embedding degrades by 20% to 40% due to preprocessing of cover images. Results also indicate that the Markov-based SPAM and Joint Entropy-based features work well for steganalysis.