Improving image steganalyser performance through curvelet transform denoising

The major challenge of feature based blind steganalysers lies in designing effective image features which give true evidence of the stego noise rather than the natural noise present in the images. Hence they report low detection accuracy in real time implementation in spite of employing 100s of features in the process. In this paper, we coin a new paradigm for detecting steganography by examining the task as a three-steps process with the following repercussions: (a) employing curvelet transform denoising as a pre-processing step that produces better stego noise residuals suppressing the natural noise residual rather than a general denoising step before feature extraction, (b) extracting various steganalytic features, both in spatial domain as well transform domain and (c) implementing the system based on an efficient classifier, multi-surface proximal support vector machine ensemble oblique random rotation forest, that provides detection rate superior to other existing classifiers. Extensive experimentation with huge database of clean and steganogram images produced from seven steganographic schemes with varying embedding rates, and using five steganalysers, shows that the proposed paradigm improves the detection accuracy substantially and proves to be a high performance strategy even at low embedding rates. This model can be employed as a preprocessing component for any image steganalyser and high performance accuracy can be obtained.