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Video copy detection based on source device characteristics: a complementary approach to content-based methods
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International Multimedia Conference archive
Proceeding of the 1st ACM international conference on Multimedia information retrieval table of contents
Vancouver, British Columbia, Canada
SESSION: Video retrieval and concept detection table of contents
Pages 435-442  
Year of Publication: 2008
ISBN:978-1-60558-312-9
Authors
Sevinc Bayram  Polytechnic University, Brooklyn, NY, USA
Husrev Taha Sencar  Polytechnic University, Brooklyn, NY, USA
Nasir Memon  Polytechnic University, Brooklyn, NY, USA
Sponsors
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

We introduce a new video copy detection scheme to complement existing content-based techniques. The idea of our scheme is based on the fact that visual media possess unique characteristics that can be used to link a media to its source. Proposed scheme attempts to detect duplicate and modified copies of a video primarily based on peculiarities of imaging sensors rather than content characteristics only. We demonstrate the viability of our scheme by both analyzing its robustness against common video processing operations and evaluating its performance on real world data. Results show that proposed scheme is very effective and suitable for video copy detection application.


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
A. J. A. Vailaya, M. Figueiredo and H.-J. Zhang. Image classification for content-based indexing. IEEE Transactions on Image Processing, 10(1):117--129, January 2001.
 
2
S. Bayram, H. T. Sencar, and N. Memon. Classification of digital camera models based on demosaicing artifacts. In to appear in Journal of Digital Investigations, 2008.
 
3
M. Chen, J. Fridrich, and M. Goljan. Digital imaging sensor identification (further study). Security, Steganography, and Watermarking of Multimedia Contents IX. Proceedings of the SPIE, 6505:65050P, February 2007.
 
4
M. Chen, J. Fridrich, M. Goljan, and J. Lukás. Source digital camcorder identification using sensor photo response non-uniformity. SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents IX, 6505:1G-1H, January 28-February 2 2007.
 
5
K. S. Choi, E. Y. Lam, and K. K.Y. Wong. Sourcecameraidentification using footprints from lens aberration. Digital Photography II. Proceedings of the SPIE, 6069:172--179, February 2006.
 
6
B. Coskun, B. Sankur, and N. Memon. Spatio-temporal transform-based video hashing. IEEE Transactions on Multimedia, 8(6):1190--1208, 2006.
 
7
A. E. Dirik, H. T. Sencar, and N. Memon. Digital single lens reflex camera identification from traces of sensor dust. In IEEE Transactions on TIFS, 2008.
 
8
X. Fang, Q. Sun, and Q. Tian. Content-based video identification: a survey. International Conference on Information Technology: Research and Education, 2003.
 
9
Z. J. Geradts, J. Bijhold, M. Kieft, K. Kurosawa, K. Kuroki, and N. Saitoh. Methods for identification of images acquired with digital cameras. SPIE, Enabling Technologies for Law Enforcement and Security, 4232:505--512, February 2001.
 
10
T. Gloe, E. Franz, and A. Winkler. Forensics for flatbed scanners. Security, Steganography, and Watermarking of Multimedia Contents IX, 6505:65051I, February 2007.
 
11
M. Goljan and J. Fridrich. Camera identification from scaled and cropped images. Proc. SPIE, Electronic Imaging, Forensics, Security, Steganography, and Watermarking of Multimedia Contents X, January 26--31 2008.
 
12
H. Gou, A. Swaminathan, and M. Wu. Robust scanner identification based on noise features. Security, Steganography, and Watermarking of Multimedia Contents IX. Proceedings of the SPIE, 6505(65050S), February 2007.
 
13
P. Indyk, G. Iyengar, and N. Shivakumar. Finding pirated video sequences on the internet. In Technical Report. Stanford University, 1999.
 
14
 
15
E. Kasutani and A. Yamada. The mpeg-7 color layout descriptor: A compact image feature description for high-speed image/video segment retrieval. IEEE International Conference on Image Processing: ICIP, 1: 674--677, October 2001.
 
16
N. Khanna, A. K. Mikkilineni, G. T. C. Chiu, J. P. Allebach, and E. J. Delp. Scanner identification using sensor pattern noise. Security, Steganography, and Watermarking of Multimedia Contents IX. Proceedings of the SPIE, 6505:65051K, February 2007.
 
17
T. Kuronuni, K. Kashino, and H. Murase. A method for robust and quick video searching using probabilistic dither-voting. International Conference on Image Processing, 2:653--656, October 2001.
 
18
K. Kurosawa, K. Kuroki, and N. Saitoh. Ccd fingerprint method identification of a video camera from videotaped images. In ICIP99, pages 537--540. Kobe, Japan, 1999.
 
19
T. V. Lanh, K.-S. Chong, S. Emmanuel, and M. S. Kankanhalli. A survey on digital camera image forensic methods. In 2007 IEEE International Conference on Multimedia and Expo, 2007.
20
 
21
Y. Li, L. Jin, and X. Zhou. Video matching using binary signature. In International Symposium on Intelligent, Signal Processing and Communication Systems, pages 317--320, 2005.
 
22
 
23
J. Lukás, J. Fridrich, and M. Goljan. Digital camera identification from sensor pattern noise. IEEE Transactions Information Forensics and Security, 1(2):205--214, 2006.
 
24
Y. Meng, E. Y. Chang, and B. Li. Enhancing dpf for near-replica image recognition. International Conference on Pattern Recognition, pages 416--423, 2003.
 
25
H. T. Sencar and N. Memon. Overview of State-of-the-art in Digital Image Forensics. World Scientific Press, 2008.
 
26
Y. Sutcu, S. Bayram, H. T. Sencar, and N. Memon. Improvements on sensor noise based source camera identification. In Proceedings of IEEE ICME, 2007.
 
27
A. Swaminathan, M. Wu, and K. J. R. Liu. Non intrusive forensic analysis of visual sensors using output images. IEEE Transactions of Information Forensics and Security, 2(1):91--106, March 2007.
 
28
L. W. Y. Lu, H.-J. Zhang and C. Hu. Joint semantics and feature based image retrieval using relevance feedback,. IEEE Transactions Multimedia, 5(3):339--346, September 2003.
29

Collaborative Colleagues:
Sevinc Bayram: colleagues
Husrev Taha Sencar: colleagues
Nasir Memon: colleagues