1Applying Transform-domain Scrambling   to Automatically Detected Faces Pavel Korshunov, Aleksei Triastcyn, and           ...
Introduction         2•Recipe    – Take simple face detection      (OpenCV)    – Combine with a privacy filter    – Apply ...
Privacy Filters        3• Simple: pixelization, masking, and  blurring   – Non-reversible   – encryption anonymization, et...
Transform-domain scrambling                                4                                          Scramblin         En...
Scrambling in JPEG                                                       5F. Dufaux and T. Ebrahimi, “Scrambling for priva...
Transform-domain scrambling                6• Pros    – Reversible method    – Does not negatively affect coding efficienc...
Subjective evaluation results                     7                                                  Detection            ...
Subjective evaluation results         8Multimedia Signal Processing GroupSwiss Federal Institute of Technology, Lausanne
Scrambled frame, morning video            9Multimedia Signal Processing GroupSwiss Federal Institute of Technology, Lausanne
Scrambled frame, evening video            10Multimedia Signal Processing GroupSwiss Federal Institute of Technology, Lausa...
Scrambled frame, evening video            11Multimedia Signal Processing GroupSwiss Federal Institute of Technology, Lausa...
Conclusion          12•Face detection played unexpectedlyimportant role    – Either use better detection or re-think      ...
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MediaEval 2012 Visual Privacy Task: Applying Transform-domain Scrambling to Automatically Detected Faces

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MediaEval 2012 Visual Privacy Task: Applying Transform-domain Scrambling to Automatically Detected Faces

  1. 1. 1Applying Transform-domain Scrambling to Automatically Detected Faces Pavel Korshunov, Aleksei Triastcyn, and Touradj EbrahimiMultimedia Signal Processing GroupSwiss Federal Institute of Technology, Lausanne
  2. 2. Introduction 2•Recipe – Take simple face detection (OpenCV) – Combine with a privacy filter – Apply filter to all detected regions in a video•Focus on the privacy filterMultimedia Signal Processing GroupSwiss Federal Institute of Technology, Lausanne
  3. 3. Privacy Filters 3• Simple: pixelization, masking, and blurring – Non-reversible – encryption anonymization, etc.• Anonymization (replacing with another object) – Non-reversible – Hard to implement• Encryption – Video alterations break the filter – Complex Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne
  4. 4. Transform-domain scrambling 4 Scramblin Entropyframe Transform g coding bitstream encoder • Seed random generator with a secret key • Randomly flip sign of 63 DCT coefficients in a scrambled macro block • During decoding, repeat the same Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne
  5. 5. Scrambling in JPEG 5F. Dufaux and T. Ebrahimi, “Scrambling for privacy protection in video surveillance systems,” IEEETrans. on Circuits and Systems for Video Technology, vol. 18, no. 8, pp. 1168–1174, Aug 2008. Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne
  6. 6. Transform-domain scrambling 6• Pros – Reversible method – Does not negatively affect coding efficiency – Scrambling strength can be controlled – Security can be insured• Cons – Must be integrated inside the encoderMultimedia Signal Processing GroupSwiss Federal Institute of Technology, Lausanne
  7. 7. Subjective evaluation results 7 Detection accuracy: 0.24Multimedia Signal Processing GroupSwiss Federal Institute of Technology, Lausanne
  8. 8. Subjective evaluation results 8Multimedia Signal Processing GroupSwiss Federal Institute of Technology, Lausanne
  9. 9. Scrambled frame, morning video 9Multimedia Signal Processing GroupSwiss Federal Institute of Technology, Lausanne
  10. 10. Scrambled frame, evening video 10Multimedia Signal Processing GroupSwiss Federal Institute of Technology, Lausanne
  11. 11. Scrambled frame, evening video 11Multimedia Signal Processing GroupSwiss Federal Institute of Technology, Lausanne
  12. 12. Conclusion 12•Face detection played unexpectedlyimportant role – Either use better detection or re-think evaluation methodology•Subjects were highly irritated withscrambling! – Make scrambling more human-friendlyMultimedia Signal Processing GroupSwiss Federal Institute of Technology, Lausanne

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