Data security is of the utmost concern of a communication system. Since the early days, many developments have been made to improve the performance of the system. PSNR of the received signal, secure transmission channel, quality of encoding used, etc. are some of the key attributes of a good system. To ensure security, the most commonly used technique is cryptography in which the message is altered with respect to a key and using the same, the encoded message is decoded at the receiver side. A complementary technique that is popularly used to insure security is steganography. The advancements in Artificial Intelligence(AI) have paved way for performing steganography in an intelligent, tamper-proof manner. The recent discovery by researchers in the field of Deep Learning(DL), an unsupervised learning network known as the Generative Adversarial Networks(GAN) has improved the performance of this technique exponentially. It has been demonstrated that deep neural networks are highly sensitive to tiny perturbations of input data, giving rise to adversarial examples. Though this property is usually considered a weakness of learned models, it could be beneficial if used appropriately. The work that has been accomplished by MIT for this purpose, a deep-neural model by the name of SteganoGAN, has shown obligation for using this technique for steganography. In this work, we have proposed a novel approach to improve the performance of the existing system using latent space compression on the encoded data. This theoretically would improve the performance exponentially. Thus, the algorithms used to improve the system's performance and the results obtained have been enunciated in this work. The results indicate the level of dominance this system could achieve to be able to diminish the difficulties in solving real-time problems in terms of security, deployment and database management.