Abstract
Recently, researchers have shown that coverless steganography is relatively safe. On this basis, to improve the payload of the coverless steganography, a novel semiconstruction coverless steganography algorithm is introduced in the paper. Firstly, web crawler technology is applied to crawl a wide range of small icons and hot news images from the Internet. These icons can be used as the training subset, and the hot news can be designed according to construction rules. Secondly, the Alex-Net network is introduced for training in the algorithm, and the adversarial samples are added to the training set. Thirdly, using the preset template, certain small icons and a hot news image are spliced into a secret carrier image according to the construction principle. The hot news image is in the top half of the carrier, and those small icons are in the bottom half. The image on the upper part of the carrier and the icons of the lower part can be connected by image and text semantics, and the semantic matching can be realized between image semantics and explanatory. The experimental results and analysis show that the proposed algorithm can resist steganalysis tools effectively and has good robustness against various image attacks. Meanwhile, the secret information payload has been greatly improved, the maximum payload can reach 180 bits of a single 512 × 512 image. This promising algorithm can be applied to build covert communications.
Highlights
Secret information is hidden by modifying the original carrier with a high embedding capacity
As a branch of information hiding, the coverless image steganography was firstly proposed by Zhou et al [4]
The original carrier is not specified in advance. e stego can be directly generated from the secret information according to certain rules [10]
Summary
Secret information is hidden by modifying the original carrier with a high embedding capacity. Different from the above algorithm, in order to improve the secret information payload, a semistructured coverless steganography algorithm based on the image and text semantic is introduced in the paper. The image semantic features are associated with the text icons through the preset template; the Alex-Net network is trained to classify text icons to build an image library, which forms a mapping relationship with secret information In this way, it can meet the application requirements and improve the visual antidetection ability; the experimental results show that the proposed algorithm in this paper has more powerful robustness and logical content; and the maximum secret information payload of a single 512 × 512 image can reach 180 bits.
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