An Analysis of Fingerprinting System Components for Video Copy Detection

Curtis Kelsey

University of Missouri

kelseyc@missouri.edu

A Fingerprinting system mobile model for video copy protection

motivation

Create Application/Database ecosystems free of copyright infringement

Reduce computational cost incurred on the provider.

Proposed technique

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

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

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 OpenCVs training [6]

As training time increases, accuracy increases in a logarithmic form

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

Modified proposed technique

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

Metrics

Precision

Recall

False Positive

False Negative

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

Ntp is the number to true positives/correct matches

Np is the total number of positives/matches

Nep is the number of expected positives/matches

Nen is the number of expected negatives

Nfp is the false positive rate

Nfn is the false negative rate

8

First things first

Eliminate Preprocessing

What was done?

Video size constrained

Frame rate constrained

Encoding bit rate constrained

Transition intensity

Calculate frame intensity

FI =

Calculate transition intensity

Determine the threshold between scenes

Determine the number of scenes

CONVERT RGB to YUV

Y` is a measure of overall luminance

Can be used instead of components

SCENE frames

Meng et al. describes multiple solutions. I use a basic luminance differencing in the temporal domain.

Threshold needs to be trained

Generate fingerprint

Use the scene frames to generate block luminance signatures of each frame

Base on ordinal ranking

Weak to affine transformations

S

//Represented with k

//Represented with m

//Represented with k

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

Results

Frames process in approx. 12.5 seconds each

Core i7

4GB DDR3

Video Size

1676 x 985

Data Rate

159kbps

results

Like hardware

1280 x 720

15,513 kbps

29 fps

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. 21462149). IEEE. Retrieved from 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

[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

[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.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

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

[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

Questions