A Mobile Steganography Method Based on Deep Learning

Steganography is one of the main methods for covert communication, while mobile phones are the most commonly used communication devices. The combination of the two has high practical significance. In recent years, steganography has developed rapidly with deep learning technologies. To improve the performance, networks evolve towards a more complex and large style, which gradually deviates from the real world scenarios with covert communication as the core, resulting in low practicability. For convenience and efficiency, a lightweight image steganography method is proposed for mobile phone. The network structure is designed in a light style, with depthwise separable convolutions utilized to reduce useless parameters and keeping a balance between accuracy and speed. Based on generative adversarial networks, the proposed method consists of a generator, a decoder, and a discriminator, which are trained together defiantly and finally advance in a spiral upward trend. To deal with various challenges in the real world, the model is deployed on mobile phones for tests. The networks used on smartphones are pruned, which indicates performance degradation. To ameliorate this problem and enhance decoding accuracy, BCH correcting codes are used in the method. The results show that the method can generate high-quality images with high speed, which meets the convenience requirements in today's world. Besides, it's worth noting that the method works without online requests. All the embedding and extracting tasks can be done by phone itself, which means this scheme is immune to eavesdropping attacks. © 2022, Editorial Department of Journal of Hunan University. All right reserved.