Unveiling the Unseen: Video Recognition Attacks on Social Software

Zhao, H; Wu, H; Bian, X; Liu, S; Cheng, G; Hu, X; Tian, Z

Unveiling the Unseen: Video Recognition Attacks on Social Software

Zhao, H Wu, H Bian, X Liu, S Cheng, G Hu, X Tian, Z

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Publisher:: SPRINGER-VERLAG SINGAPORE PTE LTD
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024, 14896 LNCS, pp. 412-432
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Zhao, H
dc.contributor.author	Wu, H
dc.contributor.author	Bian, X
dc.contributor.author	Liu, S
dc.contributor.author	Cheng, G
dc.contributor.author	Hu, X
dc.contributor.author	Tian, Z https://orcid.org/0000-0001-8905-0941
dc.contributor.editor	Zhu, T
dc.contributor.editor	Li, Y
dc.date	2024-07-15
dc.date.accessioned	2025-01-02T06:33:35Z
dc.date.available	2025-01-02T06:33:35Z
dc.date.issued	2024-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024, 14896 LNCS, pp. 412-432
dc.identifier.isbn	978-981-97-5027-6
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/182845
dc.description.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.
dc.language	en
dc.publisher	SPRINGER-VERLAG SINGAPORE PTE LTD
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartof	29th Australasian Conference on Information Security and Privacy (ACISP)
dc.relation.ispartofseries	Lecture Notes in Computer Science
dc.relation.isbasedon	10.1007/978-981-97-5028-3_21
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	46 Information and computing sciences
dc.title	Unveiling the Unseen: Video Recognition Attacks on Social Software
dc.type	Conference Proceeding
utslib.citation.volume	14896 LNCS
utslib.location.activity	AUSTRALIA, Sydney
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	in_progress	*
dc.date.updated	2025-01-02T06:33:34Z
pubs.finish-date	2024-07-17
pubs.publication-status	Published
pubs.start-date	2024-07-15
pubs.volume	14896 LNCS

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/182845