Visible to the public A Novel Image Steganography Method via Deep Convolutional Generative Adversarial Networks

TitleA Novel Image Steganography Method via Deep Convolutional Generative Adversarial Networks
Publication TypeJournal Article
Year of Publication2018
AuthorsHu, D., Wang, L., Jiang, W., Zheng, S., Li, B.
JournalIEEE Access
KeywordsBrain modeling, carrier image, composability, coverless, data encapsulation, deep convolutional generative adversarial networks, digital images, distortion, feedforward neural nets, Gallium nitride, generative adversarial networks, Image coding, image generation, image steganography method, image SWE method, learning (artificial intelligence), long-term confrontation, machine-learning-based steganalysis algorithms, Metrics, noise vector, privacy, pubcrawl, Resists, secret information, state-of-the-art image steganalysis algorithms, steganography, steganography detection, traditional embedding-based steganography, trained generator neural network model, Training, Transform coding, without embedding
AbstractThe security of image steganography is an important basis for evaluating steganography algorithms. Steganography has recently made great progress in the long-term confrontation with steganalysis. To improve the security of image steganography, steganography must have the ability to resist detection by steganalysis algorithms. Traditional embedding-based steganography embeds the secret information into the content of an image, which unavoidably leaves a trace of the modification that can be detected by increasingly advanced machine-learning-based steganalysis algorithms. The concept of steganography without embedding (SWE), which does not need to modify the data of the carrier image, appeared to overcome the detection of machine-learning-based steganalysis algorithms. In this paper, we propose a novel image SWE method based on deep convolutional generative adversarial networks. We map the secret information into a noise vector and use the trained generator neural network model to generate the carrier image based on the noise vector. No modification or embedding operations are required during the process of image generation, and the information contained in the image can be extracted successfully by another neural network, called the extractor, after training. The experimental results show that this method has the advantages of highly accurate information extraction and a strong ability to resist detection by state-of-the-art image steganalysis algorithms.
Citation Keyhu_novel_2018