The protection of video data has become a hot topic of research. Researchers have proposed a series of coding algorithms to ensure the safe and efficient transmission of video information. We propose an encryption scheme that can protect video information with higher security by combining the video coding algorithm with encryption algorithm. The H.264/AVC encoding algorithm encodes the video into multiple slices, and the slices are independent of each other. With this feature, we encrypt each slice while using the cipher feedback (CFB) mode of the advanced encryption standard (AES) with the dynamic key. The key is generated by the pseudo-random number generator (PRNG) and updated in real time. The encryption scheme goes through three phases: constructing plaintext, encrypting plaintext, and replacing the original bitstream. In our scheme, we encrypt the code stream after encoding, so it does not affect the coding efficiency. The purpose of the CFB mode while using the AES encryption algorithm is to maintain the exact same bit rate and produce a format compatible bitstream. This paper proposes a new four-dimensional (4-D) hyperchaotic algorithm to protect data privacy in order to further improve the security of video encryption. Symmetric encryption requires that the same key is used for encryption and decoding. In this paper, the symmetry method is used to protect the privacy of video data due to the large amount of video encrypted data. In the experiment, we evaluated the proposed algorithm while using different reference video sequences containing motion, texture, and objects.

The secure transmission of data within a network has received great attention. As the core of the security management mechanism, the key management scheme design needs further research. In view of the safety and energy consumption problems in recent papers, we propose a key management scheme based on the pairing-free identity based digital signature (PF-IBS) algorithm for heterogeneous wireless sensor networks (HWSNs). Our scheme uses the PF-IBS algorithm to complete message authentication, which is safer and more energy efficient than some recent schemes. Moreover, we use the base station (BS) as the processing center for the huge data in the network, thereby saving network energy consumption and improving the network life cycle. Finally, we indirectly prevent the attacker from capturing relay nodes that upload data between clusters in the network (some cluster head nodes cannot communicate directly). Through performance evaluation, the scheme we proposed reasonably sacrifices part of the storage space in exchange for entire network security while saving energy consumption.

With the development of multimedia technology, the secure image retrieval scheme has become a hot research topic. However, how to further improve algorithm performance in the ciphertext needs to be further explored. In this paper, we propose a secure image retrieval scheme based on a deep hash algorithm for index encryption and an improved 4-Dimensional(4-D)hyperchaotic system. The main contributions of this paper are as follows: (1) A novel secure retrieval scheme is proposed to control data transmission. (2) An improved 4-D hyperchaotic system is proposed to preserve privacy. (3) We propose an improved deep pairwise-supervised hashing (DPSH) algorithm and secure kNN to perform index encryption and propose an improved loss function to train the network model. (4) A secure access control scheme is shown, which aims to achieve secure access for users. The experimental results show that the proposed scheme has better retrieval efficiency and better security.

Histopathological image retrieval is a key technology for computer-aided diagnosis. However, patients are reluctant to reveal their privacy in histopathological image retrieval. In order to further improve the effectiveness and safety of histopathological image retrieval, this paper proposes a new histopathological retrieval scheme based on asymmetric residual hash (ARH) and DNA coding techniques. In this paper, we first present a novel ARH for histopathological image retrieval to improve the effectiveness of histopathological search scheme, and then we use the 5-D hyperchaotic system to protect patient privacy. Specifically, the contribution consists of four aspects: 1) A histopathology ciphertext domain search scheme was proposed to improve the performance of computer-aided diagnosis. 2) An asymmetric approach was implemented to process histopathological query points and database points, and a novel asymmetric residual hash algorithm was first proposed to improve the accuracy and speed of histopathological image retrieval. 3) The 5-D hyperchaotic system and DNA coding technique are applied to histopathological image retrieval to protect patient privacy. 4) The loss function is constructed and optimized to learn network parameters and hash codes. The simulation experiment was performed on three datasets (Kimia Path24, Kimia Path960, and Malaria), and the results proved the effectiveness of the ARH algorithm. In addition, our proposed search method can resist common types of attacks during histopathological data transmission. Specifically, the MAP of the ARH is 0.9678 on the KIMIA Path24 with the value of hyperparameter is 125 and the length of hash code is 32. The MAP of the ARH is 0.966 on the KIMIA Path960 with the value of hyperparameter is 225 and the length of hash code is 32. The MAP of the ARH is 0.9482 on the Malaria with the value of hyperparameter is 10 and the length of hash code is 24.

Hashing algorithm has attracted great attention in recent years. In order to improve the query speed and retrieval accuracy, this paper proposes an adaptive and asymmetric residual hash (AASH) algorithm based on residual hash, integrated learning, and asymmetric pairwise loss. The specific description of the AASH algorithm is as follows: (1) The integrated learning model is proposed based on transfer learning and Multi-feature fusion strategy to learn the database hash code; (2) The residual hash model is proposed based on ResNet-50 to learn the query image hash code; (3) The asymmetric pairwise loss is proposed and the parameters of the residual hash model is optimized based on the database hash code; (4) The algorithm learns the database hash code and the query image hash code in an asymmetric manner, and integrates the feature learning part and the hash coded part in one frame. The experimental results on three different datasets fully demonstrate that the proposed AASH method has better performance than most symmetric and asymmetric deep hash algorithms. Specifically, the optimal result of AASH algorithm is 0.971 on Cifar10 when the hyperparameter is 100 and the hash code length is 32. The optimal result of AASH algorithm is 0.945 on ceil images when the hyperparameter is 10 and the hash code length is 24. The optimal result of AASH algorithm is 0.945 on FD-XJ when the hyperparameter is 15 and the hash code length is 32. In addition, the algorithm verifies convergence, time loss, and effectiveness.

Nowadays, visual object tracking becomes a hotspot and difficulty to achieve a real-time and accurate target tracking, but the Siamese network has solved these difficulties because of its good tracking effect and real-time performance. The location of the target in the previous frame is the template, and the similarity matching is carried out in the search area of the current frame. However, it uses Alexnet network with simple structure and fewer layers to extract features, and just uses a score map to predict the final position of the object. Aiming at these problems, in this paper, we propose the Siamese network of fused response map that use the Alexnet network with fine tuning to extract target features, and weight fusion of score maps to estimate the final position of object. Sufficient experiments on the VOT2015 and OTB100 benchmarks validate that our tracker can improve tracking performance, and perform at 60FPS.