@conference {18243,
	title = {Modeling and analysis of ordinal ranking in content fingerprinting},
	booktitle = {Information Forensics and Security, 2009. WIFS 2009. First IEEE International Workshop on},
	year = {2009},
	month = {2009/12//},
	pages = {116 - 120},
	abstract = {Content fingerprinting provides a compact representation of multimedia objects for copy detection. This paper analyzes the robustness of the ordinal ranking module frequently used in content fingerprinting by examining the changes in ranks as local variations are introduced in feature values. Closed-form expressions to measure such sensitivity are derived when feature values are jointly Gaussian-distributed. The results show that sensitivity depends on the strength of local variation, the total number of blocks, and the correlations among block-based feature values. Experiments with both synthesized data and image data validate the analysis and provide interesting insights, inspiring an approach to reduce the sensitivity.},
	keywords = {detection;, detection;multimedia, fingerprinting;copy, Gaussian-distributed;content, identification;multimedia, objects;ordinal, processes;fingerprint, ranking;Gaussian, systems;object},
	doi = {10.1109/WIFS.2009.5386472},
	author = {Chuang,Wei-Hong and Varna,A.L. and M. Wu}
}
@article {18161,
	title = {Adaptive Detection for Group-Based Multimedia Fingerprinting},
	journal = {Signal Processing Letters, IEEE},
	volume = {14},
	year = {2007},
	month = {2007/12//},
	pages = {964 - 967},
	abstract = {Grouping strategy has been proposed recently to leverage the prior knowledge on collusion pattern to improve collusion resistance in multimedia fingerprinting. However, the improvement is not consistent, as reduced performance of the existing group-based fingerprinting schemes than the non-grouped ones is observed when the grouping does not match the true collusion pattern well. In this letter, we propose a new adaptive detection method, where the threshold for the group detection can be adjusted automatically according to the detection statistics that reflect the underlying collusion pattern. Experimental results show that the proposed adaptive detection outperforms nonadaptive detection and provides consistent performance improvement over non-grouped orthogonal fingerprinting schemes under various collusion scenarios.},
	keywords = {adaptive, computing;security, data;, detection;collusion, detection;group-based, fingerprinting;copy, fingerprinting;grouping, multimedia, of, orthogonal, pattern;detection, protection;multimedia, statistics;group, strategy;nongrouped},
	isbn = {1070-9908},
	doi = {10.1109/LSP.2007.907992},
	author = {He,Shan and M. Wu}
}
