Secure and resilient improved image steganography using hybrid fuzzy neural network with fuzzy logic

Abstract

The exponential growth in communication networks, data technology, advanced libraries, and mainly World Wide Web services has played a pivotal role in facilitating the retrieval of various types of information as needed. However, this progress has also led to security concerns related to the transmission of confidential data. Nevertheless, safeguarding these data during communication through insecure channels is crucial for obvious reasons. The emergence of steganography offers a robust approach to concealing confidential information, such as images, audio tracks, text files, and video files, in suitable media carriers. A novel technique is envisioned based on back-propagation learning. According to the proposed method, a hybrid fuzzy neural network (HFNN) is applied to the output obtained from the least significant bit substitution of secret data using pixel value differences and exploiting the modification direction. Through simulation and test results, it has been observed that the proposed methodology achieves secure steganography and superior visual quality. During the experiments, we observed that for the secret image of the cameraman, the PSNR & MSE values of the proposed technique are 61.963895 and 0.041361, respectively.