Information hiding technology hides secret information in the host signals in an invisible way and extracts it when needed [1
]. Information hiding realizes the concealment of communication and copyright protection and has received increasing attention. Digital images are often used as a cover for information hiding. The traditional image information hiding is divided into spatial-based methods and transform domain methods according to different methods. Spatial-based information hiding mainly modifies the pixel data of an image, for example, replacing the least significant bit (LSB) of an image [2
] or pixel-value difference (PVD) [4
], modifying the statistical characteristics of the host image information [5
]. Transform domain information hiding converts image into a corresponding transform domain, performing information hiding such as QT hiding method [6
], discrete fourier transform (DFT) domain information hiding method [7
], and discrete wavelet transform (DWT) domain information hiding method [8
]. What the traditional information hiding methods do is hide the secret information by modifying the cover.
The traditional information hiding method inevitably leaves modification traces on the cover, causing statistical abnormality of the cover image, so that it cannot resist the steganalysis tools. Coverless steganography has been proposed to resist steganalysis. Coverless does not mean that no cover is required, instead, the generation of secret-embedded information is directly driven by the secret information. The transmission of the secret information without modifying the cover becomes the basis of the coverless steganography. Zhang et al. [9
] divided coverless steganography into semi-constructed coverless steganography and fully constructed coverless steganography. The semi-constructive steganography specifies a preset condition of cover construction and generates a dense cover according to the secret information while following certain construction rules. Different from the semi-constructive steganography, fully constructed steganography directly uses multiple objects in different normal images. It is driven by secret data, the objects are selected to construct the secret-embedded cover with reasonable content and reasonable statistical features directly.
A digital image contains not only the information of pixels, but also many characteristics such as brightness, color, texture, edge, contour, and high-level semantics. It is possible to organize the seemingly unrelated image information in a reasonable way, by hiding the secret information through some mapping relationship, if these characteristics are described in certain way. If the corresponding original image can be found through certain mapping relationship, the secret information transmission is achieved through the original image, so that the coverless steganography without modifying the cover is realized. Therefore, the key to coverless steganography is the mapping of image information to a codebook. The construction of coverless steganography codebook is shown in Figure 1
. Different secret-embedded covers are generated by a certain mapping relationship from an image, one set (one or more secret-embedded covers) of secret-embedded covers is indexed to a codeword, and all covers construct the codewords of coverless steganography. The secret information transmission is realized by transmitting a secret-embedded cover corresponding to codewords.
The existing image coverless steganography methods use different mapping methods to construct the codewords corresponding to secret information. A codeword of coverless steganography is constructed using the mapping of the visual words (VW) of the encoding block of bag-of-words (BOW) model [10
]. Otori et al. [11
] mapped image texture to codewords using LBP encoding, Wu et al. [12
] constructed codewords using block sorting to synthesize texture. Liu et al. [13
] constructed the codewords of secret-embedded cover using generative adversarial networks (GAN).
Although the above new coverless steganography methods have different mapping methods to construct codewords, they have three characteristics in common: (1) Diversity: In order to avoid suspicion from network analysts, the secret-embedded covers are rich in forms according to the real scene; (2) Difference: There is difference between the secret-embedded covers corresponding to different codewords to ensure identifiability; (3) Completeness: There is at least one secret-embedded cover in each group in the codewords to ensure one-to-one correspondence with codeword index.
Deep learning brings new insight into image synthesis. Gatys et al. [14
] proposed the image style transfer based on convolutional neural network (CNN) and found that CNN can be used to separate the content characteristics and the style characteristics of an image. By independently processing these high-level abstracted characteristics to achieve the image style transfer effectively, a rich artistic result is obtained. However, when the input is the same, the difference between the multiple generated results is too small. This image style transfer is not suitable for constructing codewords for coverless steganography.
In order to realize the diversity and difference of the image style transfer results, Li et al. [15
] proposed a simple method which used whitening and coloring transforms (WCTs) for universal style transfer, which enjoys the style-agnostic generalization ability with marginally compromised visual quality and execution efficiency. To increase diversity and enhance visual effects, Li et al. [16
] proposed an image style transfer algorithm with added random noise based on CNN. Only one-dimensional random noise is used as the network input, the noise difference at different levels are not considered. Based on their work, we propose a diversity image style transfer network using of multilevel noise encoding and integrate it into a coverless steganography scheme. The network generates not only artistic effect image style transfer results, but also texture synthesis results with the expected difference and diversity, without changing the network structure. The network is used to construct coverless steganography codewords with various texture characteristics. The coverless steganography for secret information transmission is realized.
The main contributions of this paper include:
A generator structure of multilevel noise encoding that matches the subsequent visual geometry group-19 (VGG-19) network scale, the ability to synthesize multiple textures is enhanced.
Diversity loss is used to prevent the network from falling into local optimization and allows the network to generate diversity image style transfer results.
Residual learning is introduced to improve the network training speed significantly.
Coverless steganography and image style transfer are combined, a coverless steganography scheme is presented. The performance of our coverless steganography scheme is good in steganographic capacity, anti-steganalysis, security, and robustness.