IVForensic

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IVForensic: a digital forensics service platform for internet videos

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International Multimedia Conference archive
Proceedings of the seventeen ACM international conference on Multimedia table of contents

Beijing, China

DEMONSTRATION SESSION: Technical demonstrations session 2 table of contents

Pages 1015-1016

Year of Publication: 2009

ISBN:978-1-60558-608-3

Authors

Hao Yin	Tsinghua University, Beijing, China
Wen Hui	University of Science and Technology Beijing, Beijing, China
Quan Miao	Tsinghua University, Beijing, China
Zheng Li	Tsinghua University, Beijing, China
Chuang Lin	Tsinghua University, Beijing, China

Sponsor

SIGMULTIMEDIA: ACM Special Interest Group on Multimedia

Publisher

ACM New York, NY, USA

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ABSTRACT

IVForensic is a digital forensics service platform for Internet videos, with the aim of revealing illegal videos and preventing them from spreading over the Internet. It a) implements a flexible, secure and scalable architecture for large-scale online monitoring, b) provides an effective and efficient forensic countermeasure for widely-sourced Internet videos, c) guarantees good end-user experience such as flexible settings, simple operations and low startup latency. The results of performance evaluation using data obtained from real-world deployments demonstrate the effectiveness of the platform.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	M. Datar, N. Immorlica, P. Indyk, and V. Mirrokni. Locality-sensitive hashing scheme based on p-stable distributions. In Proceedings of the 20th Annual Symposium on Computational Geometry, pages 253--262. ACM New York, NY, USA, 2004.
	2	H. Farid and S. Lyu. Higher-order wavelet statistics and their application to digital forensics. In CVPRW. Conference on, page 94, 2003.
	3	C. Fei, D. Kundur, and R. Kwong. Analysis and design of secure watermark-based authentication systems. IEEE Transactions on Information Forensics and Security, 1(1):43--55, 2006.
	4	S. Lee and C. Yoo. Robust video fingerprinting for content-based video identification. IEEE Transactions on Circuits and Systems for Video Technology, 18(7):983--988, 2008.
	5	W. Lee and C. Hwang. A forensic computing system using a digital right management technique. In FSKD. 4th International Conference on, pages 258--262, 2007.
	6	R. Lienhart. Comparison of automatic shot boundary detection algorithms. In Proc. SPIE, pages 290--301. Citeseer, 1999.
	7	G. Pallis and A. Vakali. Insight and perspectives for content delivery networks. Commun. ACM, 49(1):101--106, 2006.
	8	A. Popescu and H. Farid. Statistical tools for digital forensics. In 6th International Workshop on Information Hiding, pages 128--147. Springer, 2004.
	9	W. Wolf. Key frame selection by motion analysis. In ICASSP. Conference Proceedings, pages 1228--1231, 1996.
	10	H. Zhang, J. Wu, D. Zhong, and S. Smoliar. An integrated system for content-based video retrieval and browsing. Pattern Recognition, 30(4):643--658, 1997.
	11	Y. Zhuang, Y. Rui, T. Huang, and S. Mehrotra. Adaptive key frame extraction using unsupervised clustering. In ICIP. Proceedings, pages 866--870, 1998.
	12	J. Zobel and A. Moffat. Inverted files for text search engines. ACM Computing Surveys, 38(2):6, 2006.