Abstract:
Nowadays, with the vigorous development of self-media services, more and more individual users upload videos freely. While bringing goodness, it also inevitably brings ev...Show MoreMetadata
Abstract:
Nowadays, with the vigorous development of self-media services, more and more individual users upload videos freely. While bringing goodness, it also inevitably brings evil. Therefore, it is particularly necessary to identify and supervise illegal videos through network stream. However, many video streaming services, such as YouTube, have applied encryption to protect users’ privacy, which makes it more difficult to analyze network stream. Many researches show that DASH (Dynamic Adaptive Streaming over HTTP) will leak information about video segmentation, which is related to the video content. Consequently, it is possible to analyze the content of encrypted video stream without decryption. Previous studies have proposed a series of encrypted video identification methods based on this. However, most of them need to wait for a long video playback time, such as more than 10s, or even wait for the entire video playback to complete the identification. In this paper, we propose a fast, lightweight, and accurate method named Long-Short Terms Frequency(LSTF) for online encrypted video identification. Experiments have proved that compared with the state-of-the-art, our method has advantages in both speed and accuracy, and even if CDN switching occurs during the video playback, it still has a high identification accuracy.
Published in: 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
Date of Conference: 24-26 May 2023
Date Added to IEEE Xplore: 22 June 2023
ISBN Information: