Paper Info
Title
An Image Style Transfer Network Using Multilevel Noise Encoding and Its Application in Coverless Steganography.
Abstract
Most of the existing image steganographic approaches embed the secret information imperceptibly into a cover image by slightly modifying its content. However, the modification traces will cause some distortion in the stego-image, especially when embedding color image data that usually contain thousands of bits, which makes successful steganalysis possible. A coverless steganographic approach without any modification for transmitting secret color image is proposed. We propose a diversity image style transfer network using multilevel noise encoding. The network consists of a generator and a loss network. A multilevel noise to encode matching the subsequent convolutional neural network scale is used in the generator. The diversity loss is increased in the loss network so that the network can generate diverse image style transfer results. Residual learning is introduced so that the training speed of network is significantly improved. Experiments show that the network can generate stable results with uniform texture distribution in a short period of time. These image style transfer results can be integrated into our coverless steganography scheme. The performance of our steganography scheme is good in steganographic capacity, anti-steganalysis, security, and robustness.
Year
DOI
Venue
2019
10.3390/sym11091152
SYMMETRY-BASEL
Keywords
Field
DocType
convolutional neural networks,coverless steganography,image style transfer,multilevel noise encoding
Steganography,Combinatorics,Pattern recognition,Convolutional neural network,Robustness (computer science),Artificial intelligence,Steganalysis,Distortion,Loss network,Mathematics,Color image,Encoding (memory)
Journal
Volume
Issue
Citations 
11
9
1
PageRank 
References 
Authors
0.39
0
6
Name
Order
Citations
PageRank
Shan-Qing Zhang144.87
Shengqi Su210.72
Li Li323734.83
QiLi Zhou411.74
Jianfeng Lu5181.54
Chin Chen Chang67849725.95