1 research outputs found
Convolutional Neural Network Steganalysis's Application to Steganography
This paper presents a novel approach to increase the performance bounds of
image steganography under the criteria of minimizing distortion. The proposed
approach utilizes a steganalysis convolutional neural network (CNN) framework
to understand an image's model and embed in less detectable regions to preserve
the model. In other word, the trained steganalysis CNN is used to calculate
derivatives of the statistical model of an image with respect to embedding
changes. The experimental results show that the proposed algorithm outperforms
previous state-of-the-art methods in a wide range of low relative payloads when
compared with HUGO, S-UNIWARD, and HILL by the state-of-the-art steganalysis.Comment: arXiv admin note: substantial text overlap with arXiv:1705.0861