IS-DGM: an improved steganography method based on a deep generative model and hyper logistic map encryption via social media networks

IS-DGM: an improved steganography method based on a deep generative model and hyper logistic map encryption via social media networks

연구 분야: Strategies

학회: Multimedia Systems

The exchange of information through social networking sites has become a major risk due to the possibility of obtaining millions of subscribers’ data at any time without the right. Multimedia security is a multifaceted field that involves various techniques and technologies to protect digital media in different contexts. As the technology evolves, so do the challenges and solutions related to multimedia security. Steganography plays a dominant role in covert communication over these social networking. In most modern adaptive steganography, the balancing between imperceptibility, payload, and security is a critical difficulty for image steganography. To this end, in this paper, we propose an improved image steganography method called IS-DGM based on a deep generative model (DGM) combined with hyper logistic map (HLM) encryption algorithm. IS-DGM consists of two strategies, steganography and recovery. In the first strategy, we have pre-processing and embedding networks. Before running the pre-processing network, the secret image is encoded using the HLM algorithm. During this phase, the encoded and the carrier images are utilized as inputs of the embedding network to boost concealment efficiency. In the second strategy, we have extraction and steganalysis networks. During this phase, the secret is extracted from the host image with the good visual quality as possible. Experimental outcomes indicate that the proposed method performs effectively in terms of perceptual quality and embedding capacity on five data sets, namely, ImageNet, CoCo2017, LFW, VoC2007, and VoC2012. In addition, it outperforms recent deep learning GAN hiding algorithms with respect to capacity, visual quality, and security. Thus, the proposed IS-DGM effectively balances good imperceptibility and increased capacity. Further, it maintains safety against histogram analysis, such as PVD analysis. Besides, the IS-DGM method increases resistance to the ROC curve analysis, including steganalysis algorithms, such as SRM, MaxSRM, Stegexpose, and Ye-Net.