A new zero-watermarking algorithm based on deep learning is proposed to improve the robustness of the zero-watermarking, in which zero-watermarking image generation and copyright verification are both completed using neural networks. First, a stylized image is generated from a host image and a logo image with a time stamp through VGG network. Then, the stylized image is encrypted by the Arnold transform and registered as a zero-watermarking image in Intellectual Property Protection (IPR). Finally, the RCNN network is designed to extract the logo image to verify the copyright of host images. The experimental results show that the security and robustness of the algorithm are better than the existing zero-watermarking algorithm.
Traditional image steganography often utilizes modifying the pixel values of the carrier image to embed secret image information. However, once the pixel value of the carrier image is modified, it will leave a trace of modification, which is easy to be discovered by a third party, thereby destroying the secret image information and causing the failure of the secret image transmission. This paper proposes a coverless image steganography algorithm based on style transfer, which uses the style of secret images to generate camouflage images and transmit them on public channels. This method does not need to modify the pixel values of carrier images, and the method can resist common attacks. Our method consists of two parts: sender and receiver. In the sending stage, we first use the style transfer technique to combine the style of the secret image with the content of the natural image to generate a camouflage image, and in the receiver stage we design a convolutional neural network (CNNSI) to extract the secret image. The training data set of CNNSI network consists of camouflage images subjected to various attacks. Experimental results show that, compared with the existing methods, the proposed method can still extract the secret image after the camouflage image is attacked. This method has better robustness and security.
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