detecting re-captured videos using shot-based photo response non-uniformity dae-jin jung

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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