With the continuous development of malicious hardware technology, HID attacks are becoming more and more capable and hidden. This paper provides a security protection scheme from the aspects of operating system login option configuration, including adding a CAPTCHA verification mechanism in the login process and an automatic lock screen setting. The scheme has been implemented and tested in a Windows 10 system based on the credential provider framework. The results show that the success rate of HID attack is close to zero in cooperation with well-educated users. Without user cooperation, the time between the user leaves the computer to the screen is locked is the only time that can be used by malicious HID devices, risking being discovered and cleared by users at other times. Compared with the existing schemes, this scheme has the characteristics of high security, low cost, and good compatibility. It can be implemented and deployed in different operating systems for computers with screens to resist HID attacks launched by a variety of malicious devices.
With the fast-speeding development of the information age, social media also provides a central channel for people to obtain information. However, the mixed-up information provided by various network platforms is hard to identify, the negative aspects of public sentiment and the spread of the rumor also make significant aspects in cultural ecological environment. At present, rumor detection is mainly focusing on deep learning, extracts and analyzes the text semantic features and then makes predictions. But, using this method to select eigenvalues always lacks varieties, it ignores the semantic integrity and the potential relationship between data. In order to improve the efficiency and accuracy in rumor detection, this research provides a method in predicting and analyzing text features, which based on the pre-training model of BERT, using the public Weibo-20 and Weibo-21 datasets, tunes the model parameters through the comparative experiments, the result shows that the processing speed and effectiveness is far more fruitful.
KEYWORDS: Video, Convolution, Digital watermarking, Video compression, Visualization, Steganography, Network architectures, Data modeling, Data hiding, Video processing
In robust video steganography, a message is embedded into a video such that video distortions are avoided while producing a stego video of imperceptible difference from the cover video. Traditional techniques achieved robustness against particular distortions but are complicated in computation and design, and rely on different compression standards. Nowadays, deep-learning-based methods can achieve impressive visual quality and robustness to attacks. We propose a framework with a channel-space attention mechanism for robust video steganography. The framework is composed of depthwise separable convolution layers that can learn channel-space segments for embedding and extraction. The secret messages are distributed across channel-space scales to increase imperceptibility and robustness to distortions. This end-to-end solution is trained with the 3-player game approach to conducting robust steganography, where three networks compete. Two of these handle embedding and extraction operations, while the third network simulates attacks and detection from a steganalyst as an adversarial network. Comparative results versus recent research show that our method is more robust against compression and video distortion attacks. Peak signal-to-noise ratio and structural similarity index were used for evaluating visual quality and demonstrate the imperceptibility of our method.
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