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Steganography has been used since centuries for concealment of messages in a cover media where messages were physically hidden. The goal in our paper is to hide digital messages using modern steganography techniques. An N * N RGB pixel secret message (either text or image) is to be transmitted in another N * N RGB container image with minimum changes in its contents. The cover image also called the carrier can be publicly visible. In this paper, along with LSB encoding, deep learning modules using the Adam algorithm are used to train the model that comprises a hiding network and a reveal network. The encoder neural network determines where and how to place the message, dispersing it throughout the bits of cover image. The decoder network on the receiving side, which is simultaneously trained with the encoder, reveals the secret image. The main aspect of this work is it produces minimal distortion to the secret message. Thus, preserving its integrity. Also, other steganography softwares cannot be used to reveal the message since the model is trained using a deep learning algorithm which complicates its steganalysis. The network is only trained once, irrespective of the different container images and secret messages given as inputs. Thus, this work has wide and secure applications in many fields.

No abstract

Steganography has been used since centuries for concealment of messages in a cover media where messages were physically hidden. The goal in our project is to hide digital messages using modern steganography techniques. An N * N RGB pixel secret message (either text or image) is to be transmitted in another N * N RGB container image with minimum changes in its contents. The cover image also called the carrier can be publicly visible. In this project, along with LSB encoding, deep learning modules using the Adam algorithm are used to train the model that comprises a hiding network and a reveal network. The encoder neural network determines where and how to place the message, dispersing it throughout the bits of cover image. The decoder network on the receiving side, which is simultaneously trained with the encoder, reveals the secret image. The main aspect of this work is it produces minimal distortion to the secret message. Thus, preserving its integrity. Also, other steganography softwares cannot be used to reveal the message since the model is trained using a deep learning algorithm which complicates its steganalysis. The network is only trained once, irrespective of the different container images and secret messages given as inputs. Thus, this work has wide and secure applications in many fields.