Abstract
Secure transmission of information across networks is of esteem importance to meet confidentiality and privacy goals and therefore is a huge concern to modern society. To prevent information from being compromised during communication, many steganography or data hiding algorithms have been introduced in recent years. Steganography refers to hiding secret information into cover files like text, images, audio etc., to secure communications. The term Image Steganography is used to describe steganography processes where images are used as cover files. Conventional steganography algorithms are observed to impose high risk of being deciphered by intelligent systems. This paper proposes a system that eliminates such a risk by hiding secret information inside stego images generated using Generative Adversarial Networks (GANs) and then safely reproducing it using an Extractor model. The experimental results of the proposed system demonstrated an accuracy of 92.34% achieved by the extractor model while retrieving the hidden information from the stego images and an overall steganography capacity of 93.75e−3 bytes/pixel for the entire system. Thus, the proposed system reduces the probability of any external intelligent system intercepting the communication and stealing information, like in the case of systems following traditional algorithms.
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Ramaneti, K., Kakani, P., Krishna, C., Rajkumar, S. (2021). Image Steganography Using GANs. In: Lee, R. (eds) Computer and Information Science 2021—Summer . ICIS 2021. Studies in Computational Intelligence, vol 985. Springer, Cham. https://doi.org/10.1007/978-3-030-79474-3_12
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DOI: https://doi.org/10.1007/978-3-030-79474-3_12
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