Steganographic Generative Adversarial Networks
This addresses the challenge of secure message hiding in images for steganography applications, but appears incremental as it applies existing DCGAN methods to enhance standard steganography algorithms.
The authors tackled the problem of generating steganography-secure image containers by proposing a model based on Deep Convolutional Generative Adversarial Networks (DCGAN), which successfully deceives steganography analyzers for use in steganographic applications.
Steganography is collection of methods to hide secret information ("payload") within non-secret information "container"). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier. In the present study, we propose a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN). This approach allows to generate more setganalysis-secure message embedding using standard steganography algorithms. Experiment results demonstrate that the new model successfully deceives the steganography analyzer, and for this reason, can be used in steganographic applications.