A New High Capacity Image Steganography Method Combined With Image Elliptic Curve Cryptography and Deep Neural Network

• Published in: IEEE access 2020, Vol.8, p.25777-25788
• Main Authors: Duan, Xintao, Guo, Daidou, Liu, Nao, Li, Baoxia, Gou, Mengxiao, Qin, Chuan
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Cryptography; Curves; DCT; Decoding; Deep learning; deep neural network; Discrete cosine transform; Discrete cosine transforms; ECC; Elliptic curve cryptography; Image quality; Image steganography; Machine learning; Neural networks; SegNet; Signal to noise ratio; Steganography
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2971528

Image steganography is a technology that hides sensitive information into an image. The traditional image steganography method tends to securely embed secret information in the host image so that the payload capacity is almost ignored and the steganographic image quality needs to be improved for the Human Visual System(HVS). Therefore, in this work, we propose a new high capacity image steganography method based on deep learning. The Discrete Cosine Transform(DCT) is used to transform the secret image, and then the transformed image is encrypted by Elliptic Curve Cryptography(ECC) to improve the anti-detection property of the obtained image. To improve steganographic capacity, the SegNet Deep Neural Network with a set of Hiding and Extraction networks enables steganography and extraction of full-size images. The experimental results show that the method can effectively allocate each pixel in the image so that the relative capacity of steganography reaches 1. Besides, the image obtained using this steganography method has higher Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity Index(SSIM) values, reaching 40dB and 0.96, respectively.