Effect of levels in Dual Tree Complex Wavelet Transform when design Universal image stego-analytic

Abstract

Universal image stego-analytic has become an important issue due to the natural images features curse of dimensionality. Deep neural networks, especially deep convolution networks, have been widely used for the problem of universal image stegoanalytic design. This paper describes the effect of selecting suitable value for number of levels during image pre-processing with Dual Tree Complex Wavelet Transform. This value may significantly affect the detection accuracy which is obtained to evaluate the performance of the proposed system. The proposed system is evaluated using three content-adaptive methods, named Highly Undetetable steGO (HUGO), Wavelet Obtained Weights (WOW) and UNIversal WAvelet Relative Distortion (UNIWARD).
The obtained precision 0.98387, 0.96659 and 0.98387 for the three content-adaptive methods, applied on BOSS image dataset, respectively. The obtained results show that number of level equals to 5 outperforms other numbers in terms of detection accuracy. Also it minimizes the ime required for both training and testing phases.