Steganography is a method of hiding secret information within non-secret information. For the purpose ofsteganography, a lot of works based on convolutional neural network(CNN) were framed recent years and theyshowed the improvement of deep learning particularly in the field of hiding information. The major key factorsthat were kept in the account by those works include enhancing the capacity, invisibility, and security. Inthis research, a work based on steganography via generative adversarial networks was utilized to increase theinvisibility and security, thus extracting that same secret image at the receiver side precisely. The focus of thisresearch was to select the best suitable optimizer for the image based Steganography. Here, Stochastic GradientDescent (SGD) and Adaptive Momentum (Adam) were compared and from the investigation, it was concludedthat Adam optimizer performs better in handling the model to improve the hiding and revealing ability.
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