A novel image steganography algorithm based on hybrid machine leaning and its application in cyberspace security

Abstract

As an important technology in the field of cyberspace security, image steganography is of great strategic significance to the research of image steganography. With the wider application of image steganography technology, there are fewer and fewer cases of image steganography using a single algorithm, and more adaptive steganography of images is used instead. According to the different sensitivities of human eyes to the foreground area and background area of the image, the key to solving this problem is to segment the foreground and background of the carrier image. Aiming at the problem of low maximum embedding capacity of traditional steganography algorithm and poor perception quality of carrier image after steganography, this paper firstly uses adaptive threshold segmentation to process carrier removal image, and divides the image into foreground and background regions. Secondly, in order to improve the embedding capacity, high-capacity steganography is used for pixels in the foreground area of the image, and low-capacity steganography is used for the background area of the image. At the same time, in order to improve the security of the secret information, hybrid machine learning is used to encrypt before the secret information is embedded, and finally BFS (Breadth First Search) is used to complete the steganography of the carrier image. The experimental results show that the PSNR (Peak Signal to Noise Ratio) value of the pictures after steganography by this algorithm is above 52.71 dB, which has high image perception quality. Better protection of confidential information.