Synchronized Detection and Recovery of Steganographic Messages with Adversarial Learning
This addresses the challenge of secure and undetectable steganography for applications in data hiding and security, representing an incremental improvement over existing adversarial methods.
The paper tackles the problem of embedding secret messages into images by designing a novel adversarial learning framework with three modules (generator, discriminator, steganalyzer) that communicate in a game, resulting in robust steganographic solutions that act like encryption.
In this work, we mainly study the mechanism of learning the steganographic algorithm as well as combining the learning process with adversarial learning to learn a good steganographic algorithm. To handle the problem of embedding secret messages into the specific medium, we design a novel adversarial modules to learn the steganographic algorithm, and simultaneously train three modules called generator, discriminator and steganalyzer. Different from existing methods, the three modules are formalized as a game to communicate with each other. In the game, the generator and discriminator attempt to communicate with each other using secret messages hidden in an image. While the steganalyzer attempts to analyze whether there is a transmission of confidential information. We show that through unsupervised adversarial training, the adversarial model can produce robust steganographic solutions, which act like an encryption. Furthermore, we propose to utilize supervised adversarial training method to train a robust steganalyzer, which is utilized to discriminate whether an image contains secret information. Numerous experiments are conducted on publicly available dataset to demonstrate the effectiveness of the proposed method.