Nowadays, due to the increasing need for cloud storage and cloud computing services, digital images need to be kept confidential in order to secure the privacy of individuals' information. Since the currently available codomain reversible data masking techniques are based on gray images, this paper introduces a data masking approach using color image channel correlation to achieve codomain reversibility. In the encryption stage, the security and homomorphism of encryption are achieved through XOR encryption and block scrambling. The data hiding processed by making full use of the texture information data of the reference channel to predict the pixel data more accurately, and an adaptive prediction error expansion technique is used to embed the encrypted information into the R, G, and B channels. The experimental results prove that the method has better security and better performance than the existing algorithms.
Aiming at the problems of distortion, low quality, and multiple transmissions of generative stego-image based on generative adversarial networks, a new generative image steganography method combing variational autoencoders (VAE) and generative adversarial networks (GAN) is proposed. This method combines the advantages of VAE and GAN. First, the binary secret information is grouped, and flag bits are added, which is converted into noise according to the mapping relationship. The noise is input into the VAE-GAN generator to obtain a high-quality stego-image group and randomly combine a new image. When extracting information, the stego-image is input to the extractor to recover the noise and converted into a binary message according to the mapping relationship. Finally, the secret information is recovered according to the flag bit. Experimental results show that the method not only improves the quality of stego-image but also has a high steganography capacity.
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