<![CDATA[Abstract In the era of information technology, it is very important to protect data and information so that irresponsible parties do not misuse it. One technique for securing data is steganography. Steganography is a technique of hiding messages in a medium. One of the media for hiding messages is pictures. However, steganography techniques can still be detected by steganalysis techniques. Steganalysis is a technique for analyzing hidden messages in steganography. Therefore this study applies image processing techniques with the Generative Adversarial Network algorithm model, which aims to manipulate images so that steganalysis techniques cannot detect hidden messages. Proof of the results of applying the Generative Adversarial Network algorithm using a web-based application containing message hiding and extraction functions. The results obtained are that the Generative Adversarial Network algorithm can be applied to create mock objects, and images can revive based on training data which is a model for how the algorithm works. In addition, the results of testing the Generative Adversarial Network algorithm were successfully applied to image steganography which functions to prevent steganalysis techniques from trying to detect messages in images. Future research is expected to be able to select steganographic images other than the results from the training data model according to the original size chosen randomly according to the selection of the user.]]>
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