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Detecting Re-captured Videos us-ing Shot-Based Photo Response
Non-Uniformity
Dae-Jin Jung
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Recent digital camcorders• Advantages• High quality• Low price• Easy usage
• Abuse• Camcorder theft
Introduction
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Camcorder theft (illegally re-captured videos)• Single largest source of [1]
• Fake DVDs• Unauthorized copies
• Causes a great loss on movie industry
Introduction
Original Recaptured
[1] Motion Picture Association Of America (http://www.mpaa.org)
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Lee et al.[2]
• Watermarking scheme• Robust against camcorder theft• Estimates the position of the pirate
• Good results• Needs embedding process
Previous Works
[2] Digital cinema watermarking for estimating the position of the pirate (2010)
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Cao et al.[3]
• Identifies recaptured images on LCD screens• Good results (EER lower than 0.5%)
• Used SVM• Not suitable for videos
Previous Works
[3] Identification of recaptured photographs on LCD screens (2010)
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Wang et al.[4]
• Detects re-projected video• Skew estimating
• Can achieve low false positive• Using many feature points
• Feature points not on the right position•Manual pre-processing is needed
Previous Works
[4] Detecting Re-Projected Video (2008)
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Recording device• Original• Analog cameras• Mainly used in movie industry• High quality, soft shades of colors
• Recaptured• Digital cameras• Small, light, easy to handle• Recapturing without being observed
Differences (Original/Recap-tured)
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Number of cameras used in record-ing• Original•Many cameras• Conversation scenes• Different purposes
• Shots have different source cameras
• Recaptured• Only 1 camera for recapturing
Differences (Original/Recap-tured)
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Different post-processing• Original• Heavy post-processing• Harmonize shots from different cameras• CGs, visual effects
• Recaptured•Minimum post-processing• Resizing• Re-compression
Differences (Original/Recap-tured)
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Shot based PRNU estimated from an original video• Has low correlation with each other• Analog camera• Many cameras in recording• Heavy post-processing
Shot based PRNU estimated from a recaptured video• Has high correlation with each other• Digital camcorder (PRNU)• 1 recording camera• Light post-processing
Resulting characteristics
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Overview
• Divide a video into shots• Estimate PRNU• PCE based recaptured video detection
Proposed method
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Shot change detection [5]
• Calculate absolute histogram difference• Good performance and fast
: Maximum gray level : Histogram of th frame
Proposed method
[5] Automatic partitioning of full-motion video (1993)
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PRNU estimation[6]
• PRNU model
• MLE method
• Codec noise removal
Proposed method
[6] Source digital camcorder identification using sensor photo response non-uniformity (2008)
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Detecting re-captured videos• PCE
• NxN PCE value matrix from N shots• NxN boolean matrix by thresholding
Proposed method
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Detecting re-captured videos• False negative correction• No fine reference pattern from sky view•Warshall’s algorithm
Proposed method
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Test set• 10 original videos• 20 shots were extracted• Full HD ~ HD
• 4 Digital camcorders• Samsung : 1 (H205BD)
• Sony : 3 (CX500, CX550, SR10)
• 40 recaptured videos
Experimental results
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Test set
Experimental results
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Re-captured video detection test• (number of true values/total) ratio in boolean
matrix• ‘1.00’ indicates a recaptured video
Experimental results
Recaptured videos
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Compression test• Quality factor(QF) : 100~60• MPEG4 (AVC/H.264)
Experimental results
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Resize test• Scaling factor (SF) : 0.9~0.3• MPEG4 (AVC/H.264)
Experimental results
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Combinational test• Common setting for re-compression• Quality factor (QF) : 80• Scaling factor (SF) : 0.5• MPEG4 (AVC/H.264)• 100% detected
Experimental results
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Automatic recaptured video detec-tion• Uses the shot based PRNU• Good results• Recompressed• Resized
Still weak against severe attacks
Conclusion
Thank you
Threshold setting• 2400 pairs of PRNU from same cam-
corders• 2400 pairs of PRNU from different cam-
corders• Threshold : 80
Appendix
Un-correctable False negative
Appendix
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