With the continuous development of multimedia, more and more digital works such as videos are spread, stored, and used in the network. In recent years, digital copyright infringement disputes have occurred frequently. The traditional copyright protection system has some problems, such as difficulty confirming copyright, monitoring infringement, and obtaining evidence for rights protection. To this end, we have designed and implemented a novel video copyright protection scheme based on the blockchain and double watermarking technology. We use the image correlation coefficient method to extract video keyframes. And we combine with Contourlet Transform domain, QR decomposition, and SIFT algorithm to improve the robustness of watermark against geometric attacks on the premise of invisibility. After that, we use Arnold Transformation (Cat Map) based on the Maximum Entropy Threshold Segmentation to encrypt the robust watermark and strengthen the security. Moreover, based on the characteristics of the fragile watermarking, we accurately locate the attacked video’s tamper position and complete the integrity authentication of the watermarked video. In addition, the hash digest of the video watermark and the user ID of the copyright owner is signed by SM2 and uploaded to the blockchain. The user can register the copyright after passing the identity authentication. We conduct tests and security analysis on the blockchain performance of the system, the performance of the commercial cryptography algorithm, and the security of the watermarking system. The experimental results show that the blockchain used in this system conforms to the industry standard, the performance of SM2 and SM3 is better than ECC-256 and SHA-256, and the system security is well guaranteed.

Underwater acoustic sensor networks have a wide range of applications in both civil and military fields, but the complex and changing underwater environment makes them vulnerable to multiple security threats. Trust mechanisms are effective ways to enhance network security and reliability. In order to improve the accuracy of trust evaluation and the detection rate of abnormal nodes, this paper proposes an adaptive trust evaluation model based on fuzzy logic. This model adopts a variable weight fuzzy comprehensive evaluation algorithm to dynamically adjust the weights of three direct trust indicators to ensure the accuracy of direct trust evaluation. Then, it uses fuzzy closeness to eliminate unreliable recommendation trust and adjusts the weight of recommendation trust through deviation to improve the accuracy of indirect trust. The simulation results show that the model can effectively improve the accuracy of trust evaluation and the detection rate of abnormal nodes. Especially when the link quality is unstable, the success rate of detecting abnormal nodes in this model is improved by more than 10% compared with the existing trust model.

At present, the Internet of Vehicles technology is developing rapidly, but in the process of networking it may be attacked by hackers. In view of the tampering attack on the user’s device and identity, we establish a multifactor authentication scheme to achieve dual identity authentication through device fingerprint authentication and PKI authentication. In response to the attack by hackers in the application of data transmission, we use the SM series of state secret algorithms for data encryption and take advantage of the immutable and decentralized characteristics of blockchain to ensure the security and integrity of data collection and transmission. Through the zero-trust security network architecture, the security level of the system in the process of data transmission is effectively improved, the identity-centered dynamic access control is carried out, the user’s behavior is monitored in real time, and the malicious nodes are screened and eliminated, which improves the stability and security of the Internet of Vehicles data transmission system. By adopting the identity authentication pass rate, fingerprint authentication pass rate, API authentication pass rate, and SPA packet transmission acceptance rate as the factor set, the evaluation level of the vehicles is calculated. The experimental results show that compared with the traditional boundary-centered security protection, our scheme can protect a wider range of application security, even if there are security problems, the loss is less. Through the training simulation of the convolutional neural network, the classification accuracy of the trust level is improved.

Underwater wireless sensor networks have a wide range of application prospects in important fields such as ocean exploration and underwater environment monitoring. However, the influence of complex underwater environments makes underwater wireless sensor networks subject to many limitations, such as resource limitation, channel openness, malicious attacks, and other problems. To address the above issues, we propose a routing scheme for underwater wireless networks based on a trust model and Void-Avoided algorithm. The proposed scheme establishes a trust model, evaluates the behavior of underwater nodes through direct trust, indirect trust, and environmental trust, and finds malicious nodes while taking into account evaluation of the channel, which provides support for the next data transmission event. The proposed scheme prioritizes the total cabling distance and introduces a two-hop availability checking model for data transmission, checking the nodes for voids and avoiding the void areas, to find the transmission path with the lowest energy consumption and lowest latency as much as possible. In this study, simulation experiments were conducted on the proposed scheme, and the results showed that the target scheme can effectively detect malicious nodes through anomalous behaviors and outperforms existing work in terms of malicious node detection rate, energy consumption, and end-to-end latency, and network performance.

Fire detection can effectively prevent the occurrence of fire. For the current fire detection methods, traditional image processing techniques such as grayscale image processing and feature extraction processing have poor anti-interference ability, weak generalization ability, and the detection results are more sensitive to data fluctuations. At present, based on the concept of deep learning, the proposed convolutional neural network processing of extracted image features has been widely used. On this basis, an improved YOLOv3 fire detection algorithm is proposed in this paper: The K-Means++ algorithm is used for clustering analysis to obtain the corresponding anchor boxes dimension, which reduces the error detection rate caused by the bounding boxes not matching the label; secondly, the resolution of the feature image is improved and the receptive field is enlarged; the image data are sharpened and the contrast is enhanced to make the data features more prominent. The experimental results show that the detection precision of the method is 97.7%, the recall rate is 98.5%, and the fps is 19, effectively solving the problem of high error detection rate and high missing rate of traditional image processing and general CNN networks for suspected flame objects.