To improve the security and privacy of audio data stored in third party servers, a novel watermarking scheme is proposed. Firstly, the authors split the host signal into frames and scramble each... Show More

To improve the security and privacy of audio data stored in third party servers, a novel watermarking scheme is proposed. Firstly, the authors split the host signal into frames and scramble each frame to get the encrypted signal. Secondly, they generate watermark bits by using the frame number and embed them into each frame of the encrypted signal, which is the data that will be uploaded to the third party servers. For the users, they can download the encrypted data and verify the data is intact or not. If the data is intact, the users decrypt the data to get the audio signal. If the audio signal is attacked in the process of transmission, they can also locate the location of the attacked frame. The experimental results show that the method proposed is effective not only for encrypted signals, but also for the encrypted signals after decryption.

Aiming at the problem that dangerous operation behaviors in the laboratory is difficult to identify by monitoring the video. An algorithm of dangerous operation behavior detection in multi-task... Show More

Aiming at the problem that dangerous operation behaviors in the laboratory is difficult to identify by monitoring the video. An algorithm of dangerous operation behavior detection in multi-task laboratory based on improved YOLOv5 structure is proposed. Firstly, the algorithm enhances, adaptively scales, and adaptively anchors box computing on the input of YOLO network. Then convolution operation is carried out to strengthen the ability of network feature fusion. Finally, the GIoU_Loss function is used at the output to optimize the network parameters and accelerate the convergence of the model. The experimental results show that the algorithm performs well in real-time head localization, head segmentation, and population regression, with significant innovation and superiority. Compared with traditional methods, this algorithm has better accuracy and real-time performance and can more effectively achieve human operation behaviors detection in laboratory application environments.

In vechcular networks, a promising approach to enhance vehicle task processing capabilities involves using a combination of roadside base stations or vehicles, there are two challenges when... Show More

In vechcular networks, a promising approach to enhance vehicle task processing capabilities involves using a combination of roadside base stations or vehicles, there are two challenges when integrating the two offloading modeth: 1) the high mobility of vehicles can easily lead to connectivity interruptions between nodes, which in turn affects the processing of the tasks that are being offloaded; and 2) vehicles on the road are not completely trustworthy, and vehicle tasks that contain private information may suffer from result errors or privacy leakage and other problems. This paper investigates the computing offloading problem for minimizing task completion delay in vehicular networks. Specifically, we design a trust model for mobile in-vehicle networks and construct a migration decision problem to minimize the overall delay of task execution for all vehicle users. The simulation results show that the scheme proposed in this paper can effectively reduce the execution delay of the task compared to the baseline scheme.

Mobile phones and computers are widely used devices these days, with almost everyone carrying a smartphone and multiple personal computing devices at their homes. Unfortunately, the perpetrator... Show More

Mobile phones and computers are widely used devices these days, with almost everyone carrying a smartphone and multiple personal computing devices at their homes. Unfortunately, the perpetrator exploits these devices for their unlawful activities. They employ various tactics such as sending phishing emails, and malicious links to harvest confidential information and exploit users. The perpetrators often leave traces on search engines, where they search for illegal materials and weapons, or send threatening emails to victims. This paper primarily focuses on locating and retrieving browsers' artifacts while considering the challenges posed by private browsing modes, which perpetrator may use to cover their tracks. The study also compares well-known search engines like Edge, Safari, and Firefox, analyzing the strengths and weaknesses of their directories. Moreover, it explores evidence extraction from smartphones, comparing the success rates between rooted or jailbroken phones and evidence obtained from browsers versus applications.

The traditional laboratory anomaly detection methods mainly focus on the hidden dangers caused by chemical leaks and other items, ignoring the impact of abnormal behaviors such as incorrect... Show More

The traditional laboratory anomaly detection methods mainly focus on the hidden dangers caused by chemical leaks and other items, ignoring the impact of abnormal behaviors such as incorrect operations and improper behavior on safety in the laboratory. This paper proposes a laboratory abnormal behavior detection method based on multimodal information fusion. The method generates a dense optical flow field of RGB image sequences based on optical flow theory and global smoothing constraints, and mines motion mode information. Meanwhile, the contour modal information of behavior is captured through convolution and adjacency matrix operations. Using decision level and proximity functions to integrate student behavior motion mode information and contour mode information, and using the maximum value as the behavior detection result. The experimental results show that the method can effectively detect abnormal behavior in the laboratory environment, with small detection errors and a specificity close to 1.00, effectively ensuring the safety of the laboratory environment.