A novel and dependable image steganography model for strengthening the security of cloud storage

Journal of Intelligent and Fuzzy Systems(2020)

引用 1|浏览13
暂无评分
摘要
Image steganography provides efficient techniques and methods for embedding secure data into an image. Researchers face many challenges in this field such as: ensuring the quality of the stego image is adequate, ensuring the hidden message is secure, increasing the hiding capacity, recovering the hidden message and the cover image losslessy, and overcoming the effects of lossy image compression on the hidden message. In this paper, we address the above challenges by proposing a new image steganography model to ensure the security of data in cloud storage. The fundamental processes within the base level of the proposed model are to preprocess both the cover image and the secret message. The cover image is transformed to the wavelet domain using integer-to-integer transform while the secret message is compressed using lossless entropy coding and then it is encrypted for additional security before embedding. The control level of the model drives the steganography process as follows. Firstly, it selects significant coefficients from the transformed cover image according to some threshold values. Then, it creates groups of 7-bits and 3-bits from non-lossy bits of selected significant coefficients and the encrypted bit stream of the secret message, respectively. Finally, the non-lossy bits in the selected significant coefficients are updated by injecting the secret bits. Through the process of steganography, the consistency between the payload of the secret message and the number of selected significant coefficients is checked. The model is validated and verified using extensive real experiments. Moreover, the performance of the proposed model is measured by comparison with other recent models.
更多
查看译文
关键词
Integer wavelet transform (IWT),embedded zero-tree wavelet (EZW),data hiding image steganography,stego-image quality
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要