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

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Multimedia Signal Processing GroupSwiss Federal Institute of Technology, Lausanne

Applying Transform-domain Scrambling to Automatically Detected Faces

Pavel Korshunov, Aleksei Triastcyn, and Touradj Ebrahimi

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Introduction

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

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Privacy Filters

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

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Transform-domain scrambling

Seed random generator with a secret key Randomly flip sign of 63 DCT coefficients

in a scrambled macro block During decoding, repeat the same

ScramblinScramblinggScramblinScramblinggTransformTransformTransformTransform Entropy Entropy

codingcodingEntropy Entropy codingcodingframe bitstream

encoder

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Scrambling in JPEG

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.

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Transform-domain scrambling

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

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Subjective evaluation results

Detection accuracy:0.24

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Subjective evaluation results

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Scrambled frame, morning video

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Scrambled frame, evening video

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Scrambled frame, evening video

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Conclusion

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