Detecting Re-captured Videos using Shot-Based Photo Response Non-Uniformity Dae-Jin Jung

 Recent digital camcorders Advantages High quality Low price Easy usage Abuse Camcorder theft Introduction 2

 Camcorder theft (illegally re-captured videos) Single largest source of [1] Fake DVDs Unauthorized copies Causes a great loss on movie industry Introduction OriginalRecaptured [1] Motion Picture Association Of America ( 3

 Lee et al. [2] Watermarking scheme Robust against camcorder theft Estimates the position of the pirate Good results Needs embedding process Previous Works 4 [2] Digital cinema watermarking for estimating the position of the pirate (2010)

 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 5 [3] Identification of recaptured photographs on LCD screens (2010)

 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 6 [4] Detecting Re-Projected Video (2008)

 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/Recaptured) 7

 Number of cameras used in recording Original Many cameras Conversation scenes Different purposes Shots have different source cameras Recaptured Only 1 camera for recapturing Differences (Original/Recaptured) 8

 Different post-processing Original Heavy post-processing Harmonize shots from different cameras CGs, visual effects Recaptured Minimum post-processing Resizing Re-compression Differences (Original/Recaptured) 9

 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 10

 Overview Divide a video into shots Estimate PRNU PCE based recaptured video detection Proposed method 11

Proposed method 12 [5] Automatic partitioning of full-motion video (1993)

 PRNU estimation [6] PRNU model MLE method Codec noise removal Proposed method 13 [6] Source digital camcorder identification using sensor photo response non-uniformity (2008)

Proposed method 14

 Detecting re-captured videos False negative correction No fine reference pattern from sky view Warshall’s algorithm Proposed method

 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 16

 Test set Experimental results 17

 Re-captured video detection test ( number of true values/total ) ratio in boolean matrix ‘1.00’ indicates a recaptured video Experimental results 18 Recaptured videos

 Compression test Quality factor(QF) : 100~60 MPEG4 (AVC/H.264) Experimental results 19

 Resize test Scaling factor (SF) : 0.9~0.3 MPEG4 (AVC/H.264) Experimental results 20

 Combinational test Common setting for re-compression Quality factor (QF) : 80 Scaling factor (SF) : 0.5 MPEG4 (AVC/H.264) 100% detected Experimental results 21

 Automatic recaptured video detection Uses the shot based PRNU Good results Recompressed Resized  Still weak against severe attacks Conclusion 22

Thank you

 Threshold setting 2400 pairs of PRNU from same camcorders 2400 pairs of PRNU from different camcorders Threshold : 80 Appendix

 Un-correctable False negative Appendix