Abstract
Video recognition attacks are a fine-grained privacy threat that can leak much private information. Moreover, due to the social nature of social software, their video preferences leak more personal information than traditional video software. Existing attack methods are unable to recognize rapidly updated videos on social software. In this paper, we propose an attack method to recognize videos watched on social software under different network environments and playback modes, and the key of the method is to restore the original length of video segments through the features of HTTP/2 protocol and TLS protocol. In addition, the method copes with the rapid update of social software videos through a video data rapid acquisition system. By evaluating our attack method’s performance under different network environments, playback modes, and operating systems, experimental results demonstrate that in a native network environment with sequential playback, the accuracy exceeds 98.70%, and the F1 score exceeds 98.51%. Even in a network environment with extra interference and users seeking playback, the accuracy exceeds 90.21%, and the F1 score exceeds 89.81%. In addition, the method shows good generalization that can be applied to software including Twitter, Facebook, and Instagram. These results demonstrate that video recognition attacks threaten user privacy and highlight the urgency of defending against such attacks.
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References
Cantor, L.: Sandvine global internet phenomena report 2023. Technical report, Sandvine (2023)
Schuster, R., Shmatikov, V., Tromer, E.: Beauty and the burst: remote identification of encrypted video streams. In: 26th USENIX Security Symposium (USENIX Security 17), Vancouver, BC, pp. 1357–1374. USENIX Association (2017)
Dubin, R., Dvir, A., Pele, O., Hadar, O.: I know what you saw last minute - encrypted HTTP adaptive video streaming title classification. IEEE Trans. Inf. Forensics Secur. 12(12), 3039–3049 (2017)
Gu, J., Wang, J., Yu, Z., Shen, K.: Traffic-based side-channel attack in video streaming. IEEE/ACM Trans. Netw. 27(3), 972–985 (2019)
Li, H., Niu, B., Wang, B.: SmartSwitch: efficient traffic obfuscation against stream fingerprinting. In: Park, N., Sun, K., Foresti, S., Butler, K., Saxena, N. (eds.) SecureComm 2020. LNICST, vol. 335, pp. 255–275. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63086-7_15
Bae, S., et al.: Watching the watchers: practical video identification attack in LTE networks. In: 31st USENIX Security Symposium (USENIX Security 22), Boston, MA, pp. 1307–1324. USENIX Association (2022)
Yang, L., Fu, S., Luo, Y., Shi, J.: Markov probability fingerprints: a method for identifying encrypted video traffic. In: 2020 16th International Conference on Mobility, Sensing and Networking (MSN), pp. 283–290. IEEE (2020)
He, J., et al.: Metroscope: an advanced system for real-time detection and analysis of metro-related threats and events via twitter. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR ’23, pp. 3130–3134. Association for Computing Machinery (2023)
Ali, R., Farooq, U., Arshad, U., Shahzad, W., Beg, M.O.: Hate speech detection on twitter using transfer learning. Comput. Speech Lang. 74, 101365 (2022)
Feng, Y., Luo, J., Ma, C., Li, T., Hui, L.: I can still observe you: flow-level behavior fingerprinting for online social network. In: GLOBECOM 2022-2022 IEEE Global Communications Conference, pp. 6427–6432. IEEE (2022)
Upadhyaya, A., Fisichella, M., Nejdl, W.: Intensity-valued emotions help stance detection of climate change twitter data. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (2023)
Chen, S., Wang, R., Wang, X., Zhang, K.: Side-channel leaks in web applications: a reality today, a challenge tomorrow. In: 2010 IEEE Symposium on Security and Privacy, pp. 191–206. IEEE (2010)
Wang, Y., Xu, H., Guo, Z., Qin, Z., Ren, K.: SNWF: website fingerprinting attack by ensembling the snapshot of deep learning. IEEE Trans. Inf. Forensics Secur. 17, 1214–1226 (2022)
Shen, M., Liu, Y., Zhu, L., Du, X., Hu, J.: Fine-grained webpage fingerprinting using only packet length information of encrypted traffic. IEEE Trans. Inf. Forensics Secur. 16, 2046–2059 (2020)
Di Martino, M., Quax, P., Lamotte, W.: Realistically fingerprinting social media webpages in https traffic. In: Proceedings of the 14th International Conference on Availability, Reliability and Security. ARES ’19, New York, NY, USA. Association for Computing Machinery (2019)
Wu, T., et al.: BehavSniffer: Sniff user behaviors from the encrypted traffic by traffic burst graphs. In: 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp. 456–464. IEEE (2023)
Chen, M., Wang, Y., Zhu, X.: Few-shot website fingerprinting attack with meta-bias learning. Pattern Recogn. 130, 108739 (2022)
Boumhand, A., Singh, K., Hadjadj-Aoul, Y., Liewig, M., Viho, C.: Network traffic classification for detecting multi-activity situations. In: 2023 IEEE Symposium on Computers and Communications (ISCC), pp. 681–687. IEEE (2023)
Li, Y., et al.: Deep content: Unveiling video streaming content from encrypted WiFi traffic. In: 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA), pp. 1–8. IEEE (2018)
Hua, W., Zhen-Hua, Y., Guang, C., Xiao-Yan, H.: Encrypted video recognition in large-scale fingerprint database. J. Software 32(10), 3310–3330 (2021)
Wu, H., Li, X., Wang, G., Cheng, G., Hu, X.: Resolution identification of encrypted video streaming based on http/2 features. ACM Trans. Multimed. Comput. Commun. Appl. 19(2), 1–23 (2023)
Belshe, M., Peon, R., Thomson, M.: Hypertext Transfer Protocol Version 2 (HTTP/2). RFC 7540 (2015)
McAllester, D.A., Schapire, R.E.: On the convergence rate of good-turing estimators. In: COLT, pp. 1–6 (2000)
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This work is supported by the National Key Research and Development Program of China (2021YFB3101403).
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Zhao, H. et al. (2024). Unveiling the Unseen: Video Recognition Attacks on Social Software. In: Zhu, T., Li, Y. (eds) Information Security and Privacy. ACISP 2024. Lecture Notes in Computer Science, vol 14896. Springer, Singapore. https://doi.org/10.1007/978-981-97-5028-3_21
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DOI: https://doi.org/10.1007/978-981-97-5028-3_21
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