Most of current image steganography algorithms are founded on the principle of data embedding, the data embedding operation always inevitably lead to the distortion of the cover image and liable to catch suspicion of attackers. Aiming at this problem, an image steganography without data embedding utilizing style transfer and zero-watermarking is introduce in this paper for the security of secret information transmission. First, the sender makes Quaternion Polar Harmonic Fourier transfer on the original image to obtain Quaternion Polar Harmonic Fourier moments(QPHFMs), and conduct XOR operation between the secret information to be transmitted and these generated moments, resulting in the key watermark for confidential information transmission. Subsequently, the original cover image is modified by utilizing the style transfer network to obtain a stylized image, which would be transmitted over a public channel. On the receiver side, the elaborately designed de-stylized network is employed to reconstruct the original image. Then, the QPHFMs would be re-obtained with Quaternion Polar Harmonic Fourier transfer on the reconstructed image, and the secret information are extracted by XOR operation between the re-obtained QPHFMs of the reconstructed original image and the key watermarks. There is no embedding operation of secret information in the whole secret information transmission process. Extensive experimental results indicate that the proposed scheme can implement the task of secret transmission safely and successfully.
With the in-depth development of informatization, the Internet has become an important position for data security protection, an important network terminal of key infrastructure, and an important target for the infiltration and deep latent of hostile forces. In view of the risk of disclosure of important national and enterprise information caused by illegal transmission of important files by internal personnel, this paper studies the key technologies of content security based on NLP, and proposes a text classification method based on label tree, which effectively improves the accurate management of terminal data.
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