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Jeffrey Bloom Dialogic Research (Some work done while at Thomson) Understudied Constraints Imposed by Watermarking Applications 1 With post-presentation.

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Presentation on theme: "Jeffrey Bloom Dialogic Research (Some work done while at Thomson) Understudied Constraints Imposed by Watermarking Applications 1 With post-presentation."— Presentation transcript:

1 Jeffrey Bloom Dialogic Research (Some work done while at Thomson) Understudied Constraints Imposed by Watermarking Applications 1 With post-presentation comments added in green. Workshop on Multimedia, Mathematics, and Machine Learning II Banff International Research Station for Mathematical Innovation and Discovery Banff, Alberta July 5-10, 2009

2 Understudied Problems  Watermark embedding in the compressed domain  Watermark detection in the compressed domain 2

3 “Compressed Domain Watermarking” Decode Watermark Embedding Original Compressed Stream Marked Compressed Stream Encode Entropy Decode Watermark Embedding Original Compressed Stream Marked Compressed Stream Entropy Encode Watermark Embedding Original Compressed Stream Marked Compressed Stream 3 This slide discusses the evolution of watermarking compressed streams: from full decompression to partial decompression to true compressed domain watermarking; the last of which is the subject of the first part of this presentation. For situations where partial decode is too expesive. Partial Decode

4 Stream Replacement Watermark Embedding … … 4 Shows the concept of stream replacement watermarking. A chunk of the stream is replaced with different data (in brown). But where does the brown data come from? Need to step back and review some basic frameworks on the next few slides.

5 Watermarking Frameworks Blind EmbeddingEmbed Informed EmbeddingEmbed Analyze Blind DetectionDetect Informed DetectionDetect Analyze 5 It is this last case (informed embedding / informed detection) that represents the application that motivates this work. An informed embedder can analyze the content off-line and generate side information for the embedder and for the detector. Where does the D come from?

6 2-stage Embedding Analysis Stream Embedding Entropy Decode Full Decode Analysis 6 The Analyze part of the embedder can be separated from the Embed part. The analysis can take place in a powerful server where it can examine the pixel data, the compressed domain syntax elements, and the entropy encoded bitstream.

7 Example: Blu-ray BD Player Virtual Machine Media Transform Self-Protecting Digital Content 7 SPDC is described by CRI in their white paper Use of this technology in Blu-ray is discussed at among other places.

8 SPDC  Multiple values for each repair  Enables forensic watermarking during repair Original Compressed Stream Damaged Compressed Stream Preprocess Repair Info Damaged Compressed Stream Stream Replacement Repair Info Repaired Compressed Stream 8 Damaged compressed stream is not valuable to a pirate. Damage is done off-line, repair is done at play time. Repair info must be carefully protected.

9 Identifying the hard problem  Blu-ray  MPEG2, VC1, H.264/AVC  H.264/AVC  CAVLC - Context-adaptive variable-length coding  CABAC - Context-based adaptive binary arithmetic coding  VLC case has been addressed  CABAC is hard  Arithmetic Code  Context Adaptive 9 Blu-ray supports three compression standards VC1 is not widely used H.264 supports two entropy encoding schemes Both MPEG2 and CAVLC

10 Will be more widely needed  This scenario is not limited to Blu-ray  CABAC-encoded H.264 is becoming widely deployed  High volume watermarking systems will not have the luxury of doing an entropy-decode/watermark/entropy-encode cycle Stream Replacement CABAC Encoded Bitstream For adoption of watermarking in high volume network applications, this is an important and understudied problem 10

11 Mobile Video Handoff Points Content Owner eg., MTV Content Provider eg., Hulu Content Delivery Network eg., Akamai, Internap Backbone Network eg., Global Crossing Content Delivery Network eg., back to Internap Mobile Network Operator eg., AT&T Local Access Network eg., Rogers Wireless Each participant will optimize the content for its own network. This often involves transcoding. Watermarks can be used to help track content. Detectors distributed throughout multiple networks. 11 What happens when I want to watch The Daily Show on my mobile phone from here in Banff? Many different people “touch” the content. This slide shows an example of the how the video gets to my phone.

12 Watermarking Challenge  Tracking watermark embedded by content owner  Embedding can be done in any convenient domain  Watermark detection at various points in the network for tracking  Watermark must be recoverable from any compressed domain without decoding  MPEG2  H.263  MPEG4  H.264/AVC 12

13 Watermarking Challenge  Assume that we can do entropy decode EncodeDecodeEncode DecodeDetect pixels  Intermediate transcodes look like noise  model as a single transcode plus noise  Information is in there DecodeEncode pixels 13 At any point, the stream can be decoded and the watermark recovered from the pixels Conceptually, we can consider a transcoder as a decode/encode pair with pixels in the middle. Note the colors indicate matching encode/decode pairs (same coding standard). Consider pixels before the red encode and the pixels after the red decode. The pixels after are the same as the pixels before plus coding noise from the red encode. Transcoder

14 Watermarking Challenge  Assume that we can do entropy decode EncodeDecodeEncode DecodeDetect pixels  Intermediate transcodes look like noise  model as a single transcode plus noise  Information is in there DecodeEncode pixels 14 Transcoder

15 Watermarking Challenge  Assume that we can do entropy decode Encode DecodeDetect pixels  Intermediate transcodes look like noise  model as a single transcode plus noise  Information is in there Decode pixels 15

16 Watermarking Challenge  Assume that we can do entropy decode EncodeDecodeEncode DecodeDetect pixels  Intermediate transcodes look like noise  model as a single transcode plus noise  Information is in there pixels pixels + noise 16 Transcoder

17 Summary  Watermark embedding in the entropy-coded compressed domain  Watermark detection in the compressed domain after uncontrolled transcoding For adoption of watermarking in high volume network applications, this is an important and understudied problem For adoption of watermarking for tracking in network applications, this is an important and understudied problem 17


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