Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
Videntifier™ forensic: robust and efficient detection of illegal multimedia
MM '09 Proceedings of the 17th ACM international conference on Multimedia
GPU acceleration of Eff2 descriptors using CUDA
Proceedings of the international conference on Multimedia
Second ACM international workshop on multimedia in forensics, security and intelligence (MiFor 2010)
Proceedings of the international conference on Multimedia
NV-Tree: nearest neighbors at the billion scale
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
A multimedia analytics framework for browsing image collections in digital forensics
Proceedings of the 20th ACM international conference on Multimedia
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Identifying videos on seized hard drives and other storage devices is a very tedious and time consuming task for forensic investigators. In particular, the vast amount of available material on the Internet and the large storage capacities of today's hard drives have become a strong headache for them. Videntifier" Forensic is a recent service for forensic video identification, which is based on state-of-the-art high-dimensional descriptors and high-dimensional indexing. In this paper we describe how Videntifier" Forensic tackles very large collections of video material and how robust it is towards standard modifications. We then present measurements that involve four different datasets and three collection sizes of up to 25,000 hours of video content. Our results show that Videntifier" Forensic scales very well, both in terms of the efficiency and effectiveness of the service.