Abstract
The key task of Steganalyzer is to identify if a carrier is carrying hidden information or not. Blind Steganalysis can be tackled as two-class pattern recognition problem. In this paper, we have extracted two sets of feature vectors from discrete wavelet transformation domain of images to improve performance of a Steganalyzer. The features extracted are histogram features with three bins 5, 10, and 15 and Markov features with five threshold values 2, 3, 4, 5, 6, respectively. The performance of two feature sets is compared among themselves and with existing Farid discrete wavelet transformation features based on parameter classification accuracy using neural network back-propagation classifier. In this paper, we are using three Steganography algorithms outguess, nsF5 and PQ with various embedding capacities.
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Saini, M., Chhikara, R. (2019). Performance Evaluation of Features Extracted from DWT Domain. In: Hoda, M., Chauhan, N., Quadri, S., Srivastava, P. (eds) Software Engineering. Advances in Intelligent Systems and Computing, vol 731. Springer, Singapore. https://doi.org/10.1007/978-981-10-8848-3_25
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DOI: https://doi.org/10.1007/978-981-10-8848-3_25
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