A Comprehensive Review on Steganography Techniques for Text, Images, and Audio

In audio steganography, artificial intelligence (AI) can boost system security by improving the accuracy and efficiency of encoding and decoding activities. AI techniques like Convnets, GANs, or generative adversarial networks (CNNs), which may also be taught to optimize the embedding approach to have the least detrimental effect on audio quality, can be used to find the best locations to conceal sensitive information inside an audio recording. Using AI-based sound steganography can provide a greater level of security compared to traditional approaches since it makes the procedure more reliable and unlikely to be discovered. However, difficulties like the need for a significant quantity of training data and the possibility of overfitting must be addressed in order to guarantee reliability and accuracy. Through the creation of an audio steganography technique based on artificial intelligence (AI), this research seeks to increase system security. Reviewing current steganography techniques for text, pictures, and audio and determining their advantages and disadvantages are among the goals of this work. The next step is to build and deploy a new AI-based steganography methodology that can successfully conceal hidden messages in audio signals using machine learning techniques in conjunction with currently used audio steganography techniques. The device will also use an encryption design method to further boost security. We'll evaluate a proposed system's effectiveness and resilience to various assaults, contrasting it with current audio steganography techniques and pointing out any shortcomings. Through the application of sophisticated AI-based steganography techniques, the results of this research will help in the creation of even more secure systems.