The secret behind instant messaging: video identification attack against complex protocols

Abstract People conveniently share and watch videos through Instant Messaging(IM) software, which is likely to reveal their preferences. Identifying IM video content can enable attackers to snoop on user privacy. Existing methods identify videos based on the features embodied in the DASH stream. However, IM software does not transmit video using DASH. IM software uses various transmission protocols or even private protocols for video transmission, which poses a challenge for video content identification. In this paper, we propose a video content identification framework for IM software, which obtains video content by extracting unique and stable features of videos as transmission fingerprints and matching them in a video fingerprint database. We evaluate the method on two popular IM software. The experimental results show that our method has an accuracy of 98.05% and 99.49% for ciphertext videos transmitted over GQUIC protocol and HTTPS protocol, respectively, and even reaches 100% identification accuracy for plaintext videos. Furthermore, the experimental results outperform the existing methods.