Aiming at the problem that the existing 3D model information hiding algorithms cannot effectively resist uneven compression, a multi-carrier low-density information hiding algorithm based on multiple fusion states was proposed. Firstly, multiple 3D models were positioned, oriented and stereotyped by translation and scaling. Secondly, the 3D models were rotated at different angles and merged by using the center point as merging point to obtain multiple fusion states. Thirdly, local height and Mean Shift clustering analysis were used to divide the energy of the vertices of the fusion state model, obtaining the vertices with different energies. Finally, by changing the vertex coordinates, the secret information changed by Arnold scrambling was quickly hidden in multiple fusion states and 3D models. Experimental results show that the proposed algorithm is robust against uneven compression attacks and has high invisibility.

Aiming at the problem that some 3D model-based information hiding algorithms are incompetent against combined attacks, a new strategy based on feature point labeling and clustering was proposed. Firstly, edge folding was adopted to achieve mesh simplification and all the vertexes were labeled in order by their energy level. Secondly, the ordered vertexes were clustered and re-ordered by using local height theory and Mean Shift clustering analysis. Lastly, hidden information and cover model carrier information were optimized, matched and modified by Logistic chaos mapping scrambling and genetic algorithm, completing the final hiding. The data in hiding areas were labeled and screened locally and globally according to the energy weight, which is good for the robustness and transparency of the algorithm. The experimental results show that, compared with 3D information hiding algorithms based on inscribed sphere and outer skeleton, the robustness of the proposed algorithm against single or joint attacks is significantly improved, and it also has the same degree of invisibility.

To solve the synchronization problem of audio and video, an improved algorithm of audio-video synchronization coding based on H.264 inter-frame prediction was proposed. The algorithm introduced the concept of variable code length. The audio encoding data was divided into several code groups, and each code group had 2 or 3 bits of embedded data. In the stage of H.264 inter-frame prediction, the mappings between various variable size blocks and the data of code groups were based on formula. The coding method was dynamically determined for the macro block modes coding according to embedded data, and a proposed decoding method could extract the corresponding data according to the mapping relationship. Finally, the 4×4 macro block mode was used to indicate the end of the audio data.The experimental results show that the proposed algorithm enables the Peak Signal-to-Noise Ratio (PSNR) of video samples to reduce by 0.031dB, the bit rate to increase by 5.16% and the overhead to increase by 1.97%, but the embedded audio data can be correctly and completely extracted. Therefore,the algorithm can implement the synchronization of audio and video coding while increasing the data embedding capacity, maintaining the quality of video, ensuring the correctness and completeness of the data.

The pros and cons of the existing encryption algorithm for Wireless Sensor Network (WSN) were analyzed. A chaotic block cipher based on chaos box was proposed for WSN in accordance with its security request. Random number was generated by mainly using the chaotic aperiodicity, unpredictability and other related characteristics, and corresponding S box was designed by using random number. Finally, conclusion was made by comparing RC5 and RC6 at statistical performance and energy consumption. The results show this encryption scheme has better performance for WSN.