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Applying Transform-domain Scrambling
   to Automatically Detected Faces

 Pavel Korshunov, Aleksei Triastcyn, and
           Touradj Ebrahimi



Multimedia Signal Processing Group
Swiss Federal Institute of Technology, Lausanne
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 filter


Multimedia Signal Processing Group
Swiss Federal Institute of Technology, Lausanne
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
Transform-domain scrambling                                4




                                          Scramblin         Entropy
frame               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
Scrambling in JPEG                                                       5




F. Dufaux and T. Ebrahimi, “Scrambling for privacy protection in video surveillance systems,” IEEE
Trans. 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
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 encoder


Multimedia Signal Processing Group
Swiss Federal Institute of Technology, Lausanne
Subjective evaluation results                     7




                                                  Detection
                                                  accuracy:
                                                  0.24




Multimedia Signal Processing Group
Swiss Federal Institute of Technology, Lausanne
Subjective evaluation results         8




Multimedia Signal Processing Group
Swiss Federal Institute of Technology, Lausanne
Scrambled frame, morning video            9




Multimedia Signal Processing Group
Swiss Federal Institute of Technology, Lausanne
Scrambled frame, evening video            10




Multimedia Signal Processing Group
Swiss Federal Institute of Technology, Lausanne
Scrambled frame, evening video            11




Multimedia Signal Processing Group
Swiss Federal Institute of Technology, Lausanne
Conclusion          12




•Face detection played unexpectedly
important role
    – Either use better detection or re-think
       evaluation methodology


•Subjects were highly irritated with
scrambling!
    – Make scrambling more human-friendly



Multimedia Signal Processing Group
Swiss Federal Institute of Technology, Lausanne

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

  • 1. 1 Applying Transform-domain Scrambling to Automatically Detected Faces Pavel Korshunov, Aleksei Triastcyn, and Touradj Ebrahimi Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne
  • 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 filter Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne
  • 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. Transform-domain scrambling 4 Scramblin Entropy frame 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. Scrambling in JPEG 5 F. Dufaux and T. Ebrahimi, “Scrambling for privacy protection in video surveillance systems,” IEEE Trans. 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. 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 encoder Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne
  • 7. Subjective evaluation results 7 Detection accuracy: 0.24 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne
  • 8. Subjective evaluation results 8 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne
  • 9. Scrambled frame, morning video 9 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne
  • 10. Scrambled frame, evening video 10 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne
  • 11. Scrambled frame, evening video 11 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne
  • 12. Conclusion 12 •Face detection played unexpectedly important role – Either use better detection or re-think evaluation methodology •Subjects were highly irritated with scrambling! – Make scrambling more human-friendly Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne