1. Curtis Kelsey University of Missouri kelseyc@missouri.edu A FINGERPRINTING SYSTEM MOBILE MODEL FOR VIDEO COPY PROTECTION

2. MOTIVATION Create Application/Database ecosystems free of copyright infringement Reduce computational cost incurred on the provider.

3. PROPOSED TECHNIQUE Use a modified pairwise boosting on visual Viola-Jones features to learn top-M discriminative filters on a mobile platform for querying.

4. CHARACTERISTIC ANALYSIS Benefits As accurate as the time spent training Allows for poor false positive rate Weaknesses All classifiers must have a high detection rate

5. OPENCV HARRTRAINING (IMPLEMENTATION ANALYSIS) Training the classifier requires: Negative samples for training/testing Positive samples for training/testing Training Time ~90 minutes w/ 1350+ and 5500- images [5] Classifier Accuracy > 5000 false detections per 1.3 billion [5] Naotoshi Seo extensively tests OpenCV’s training [6] As training time increases, accuracy increases in a logarithmic form

6. FEASIBILITY Can we use cascading classifiers on a mobile device? No Why? Video Data is unknown until submission. Classifier training cannot be done in real- time What now Use another fingerprinting technique for the mobile platform

7. MODIFIED PROPOSED TECHNIQUE Use a modified block-based luminance signature generated by a client for submission to a server for copy detection.

8. METRICS In a system attempting to filter copyrighted intellectual property, the false negative rate can be discarded, giving the benefit of the doubt to the user uploading video into your environment. X

9. FIRST THINGS FIRST Eliminate Preprocessing What was done? Video size constrained Frame rate constrained Encoding bit rate constrained

10. TRANSITION INTENSITY

11. CONVERT RGB TO YUV Y` is a measure of overall luminance Can be used instead of components

12. SCENE FRAMES Meng et al. describes multiple solutions. I use a basic luminance differencing in the temporal domain. Threshold needs to be trained

13. GENERATE FINGERPRINT Use the scene frames to generate block luminance signatures of each frame Base on ordinal ranking Weak to affine transformations

15. SUBMITTING THE FINGERPRINT POST fingerprint to php script via internet Use Direct Hashing Algorithm (DHA) previously presented. Hash fingerprints Insert into a standard hash table if query returns no match Query up to hamming distance of 2

16. RESULTS Frames process in approx. 12.5 seconds each Core i7 4GB DDR3 Video Size 1676 x 985 Data Rate 159kbps

17. RESULTS Like hardware 1280 x 720 15,513 kbps 29 fps

18. REFERENCES [1] Lian, H. C., Li, X. Q., & Song, B. (2011). A fingerprinting system for video copy detection. Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on (Vol. 4, pp. 2146–2149). IEEE. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6019957 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6019957 [2] Viola, P. (2001). Rapid object detection using a boosted cascade of simple features., 2001. CVPR 2001. Proceedings of the. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=990517 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=990517 [3] Zhang, Z., Cao, C., & Zhang, R. (2010). Video copy detection based on speeded up robust features and locality sensitive hashing. Automation and Logistics (, 13-18. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5585375 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5585375 [4] Meng, J., Juan, Y., & Chang, S.-fu. (1995). Scene Change Detection in a MPEG Compressed Video Sequence 2. Previous Approaches 3. MPEG Compression Standard. Symposium A Quarterly Journal In Modern Foreign Literatures, 2419 (February), 1-12. Retrieved from http://csce.uark.edu/~jgauch/library/Video-Segmentation/Meng.1995.pdfhttp://csce.uark.edu/~jgauch/library/Video-Segmentation/Meng.1995.pdf [5] Adolf, Florian. How-to build a cascade of boosted classifiers based on Haar-like features. Retrieved from http://lab.cntl.kyutech.ac.jp/~kobalab/nishida/opencv/OpenCV_ObjectDetection_HowTo.pdf http://lab.cntl.kyutech.ac.jp/~kobalab/nishida/opencv/OpenCV_ObjectDetection_HowTo.pdf

19. REFERENCES CONT.… [6] Seo, Naotoshi. Tutorial: OpenCV haartraining (Rapid Object Detection With A Cascade of Boosted Classifiers Based on Haar-like Features). Retrieved from http://note.sonots.com/SciSoftware/haartraining.html http://note.sonots.com/SciSoftware/haartraining.html [7] Mohan, R. (1998). Video sequence matching. Acoustics, Speech and Signal Processing, 1998., 3697-3700. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=679686 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=679686

20. QUESTIONS