Reversible Image Steganography Scheme Based on a U-Net Structure

• Published in: IEEE access Vol. 7; pp. 9314 - 9323
• Main Authors: Duan, Xintao, Jia, Kai, Li, Baoxia, Guo, Daidou, Zhang, En, Qin, Chuan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Decoding; deep learning; Embedding; Feature extraction; Gallium nitride; Image coding; Image reconstruction; Information security; Layouts; Machine learning; Neural networks; Receivers; reversible image steganography; Steganography; Transforms; U-Net structure
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2891247

Traditional steganography methods often hide secret data by establishing a mapping relationship between secret data and a cover image or directly in a noisy area, but has a low embedding capacity. Based on the thought of deep learning, in this paper, we propose a new image steganography scheme based on a U-Net structure. First, in the form of paired training, the trained deep neural network includes a hiding network and an extraction network; then, the sender uses the hiding network to embed the secret image into another full-size image without any modification and sends it to the receiver. Finally, the receiver uses the extraction network to reconstruct the secret image and original cover image correctly. The experimental results show that the proposed scheme compresses and distributes the information of the embedded secret image into all available bits in the cover image, which not only solves the obvious visual cues problem, but also increases the embedding capacity.