Abstract: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.

Abstract:In order to improve the data embedding capacity and the communication efficiency of coverless information hiding algorithm, addressing the advantages of semi-structured coverless information hiding algorithm，this paper introduces a semi-structured coverless information hiding algorithm based on the behavioral habits of social platforms. The specific idea of the algorithm is to build a one-to-one mapping relationship between icons and secret messages in a small icon library. According to certain principles，some small icons are montaged a picture, the secret information can be expressed by the splicing picture, and the transmission of secret messages is realized by delivering the spliced pictures. In order to improve the recognition rate of small icons and the anti-interference ability of the whole hidden communication system, convolutional neural network is also introduced to train and classify the icons in the icon library, and the interference samples are introduced as training samples set. The experimental results show that the algorithm has good anti-attack ability and the hiding capacity can be improved, and therefore, the algorithm can be used in covert communication.

Abstract:Statistical analysis based on complexity histogram is a powerful steganalytic technique for detecting the presence of secret message embedded in the digital images using Bit-Plane Complexity Segmentation(BPCS).Therefore to improve steganographic security,