Survey of research opportunities that use artificial intelligence in image steganography

Steganography conceals ”secrets” within an convenient and expedient multimedia carrier. The carrier could be text (i.e., not plain text), images, audio and/or video files (i.e., carrier channels). The fact that concealed information is contained in the otherwise ordinary and mundane carrier file is known only by the sender-receiver pair. Only they share the existence of the secret. Images are the most popular (i.e., multimedia) carriers because of their inherent property that enables better obfuscation. Content adaptive image steganography is a new trend in the field for messaging secrets inside unsuspected image file transfers. As the name suggests, the embedding locations are altered adaptively depending on the image content that optimizes the decision of choosing a location inside the carrier so that an embedding is not discernible (i.e., additive distortion is minimized). Herein, we critique the various approaches used for content-adaptive image steganography which can be broadly categorized as CNN-based, GAN-based, along with minimizing additive distortion function-based. We provide a brief historical account toward better anticipating the future research opportunities in terms of properties, and evaluation metrics. A summary table of these past and future directions is provided. Moreover, we highlight trends along with their concomitant advantages and disadvantages toward identifying opportunity gaps.