Security Enhancement in Image Steganography using Generative Adversarial Networks

The technique of concealing information within photos, videos, music etc. is called image steganography. Steganography excels in that it does not disclose the presence of any data as opposed to other methods such as cryptology which only try to prevent reading the said data. A major factor in assessing the steganography algorithms is its security. Steganography has subsequently earned remarkable ground in the drawn-out battle with steganalysis. Steganography must be able to resist identification by steganalysis algorithms in order to boost the reliability of image steganography. Conventional steganography methods embed secret data into an image, which ultimately leaves a mark of alteration that can be detected by increasingly advanced AI-based steganalysis algorithms. Within this study, the authorscompare a method of concealing secret information within photos utilizing generative adversarial networks with conventional cryptography techniques. It is hypothesized that this technique has a clear potential to challenge best-in-class steganalysis measurements by producing high performing and increasingly stable payloads.