Privacy Protection for Life-log Video Jayashri Chaudhari, Sen-ching S. Cheung, M. Vijay Venkatesh...
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Transcript of Privacy Protection for Life-log Video Jayashri Chaudhari, Sen-ching S. Cheung, M. Vijay Venkatesh...
![Page 1: Privacy Protection for Life-log Video Jayashri Chaudhari, Sen-ching S. Cheung, M. Vijay Venkatesh Department of Electrical and Computer Engineering Center.](https://reader038.fdocuments.in/reader038/viewer/2022110101/56649e7c5503460f94b7ed6c/html5/thumbnails/1.jpg)
Privacy Protection for Life-log Video
Jayashri Chaudhari, Sen-ching S. Cheung, M. Vijay Venkatesh
Department of Electrical and Computer EngineeringCenter for Visualization and Virtual Environment
University of Kentucky, Lexington, KY 40507
SAFE 2007 (11-13 April), Washington, DC
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Outline
Motivation and Background Proposed Life-Log System Privacy Protection Methodology
Face detection and blockingVoice segmentation and distortion
Experimental Results Conclusion
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What is a Life-Log System?
Applications include• Law enforcement
• Police Questioning
• Tourism
• Medical Questioning
• Journalism
“A System that records everything, at every moment and everywhere you go”
Existing Systems/work
1) “MyLifeBits Project”: At Microsoft Research
2) “WearCam” Project: At University of Toronto, Steve Mann
3) “Cylon Systems”: http::/cylonsystems.com at UK (a portable body worn surveillance system)
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Technical Challenges
Security and Privacy Information management and storage Information Retrieval Knowledge Discovery Human Computer Interface
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Technical Challenges
Security and Privacy Information management and storage Information Retrieval Knowledge Discovery Human Computer Interface
![Page 6: Privacy Protection for Life-log Video Jayashri Chaudhari, Sen-ching S. Cheung, M. Vijay Venkatesh Department of Electrical and Computer Engineering Center.](https://reader038.fdocuments.in/reader038/viewer/2022110101/56649e7c5503460f94b7ed6c/html5/thumbnails/6.jpg)
Why Privacy Protection?
Privacy is fundamental right of every citizen There are no clear and uniform rules and
regulations regarding video recording Emerging technologies threaten privacy right People are resistant toward technologies like
life-log Without tackling these issues the deployment of
such emerging technologies is impossible
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Research Contributions
Practical audio-visual privacy protection scheme for life-log systems
Performance measurement (audio) onPrivacy protectionUsability
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Proposed Life-log System
“A system that protects the audiovisual privacy of the persons captured by a portable video recording device”
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Privacy Protection Scheme
Design Objectives
• Privacy• Hide the identity of the subjects being captured
• Privacy verses usefulness: • Recording still should convey sufficient information to be useful
• Speed• Protection scheme should work in real time.
√ Usefulness× Privacy
× Usefulness√ Privacy
√ Usefulness√ Privacy
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System Overview
audio
Audio Segmentation
Audio Segmentation
Audio Distortion
Audio Distortion
Face Detection and
Blocking
Face Detection and
Blocking
videoSynchronization & Multiplexing
Synchronization & Multiplexing
storage
S
P
S: Subject (The person who is being recorded)
P: Producer (The person who is the user of the system)
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Voice Segmentation and distortion
Statek=Statek-1 or Subject or Producer
Windowed
Power, Pk
Computation
Windowed
Power, Pk
ComputationPk <TSPk <TS Pk <TU
Pk <TU
Y Y
Statek= Producer
Statek= Subject
Storage
Pitch Shifting
We use the PitchSOLA time-domain pitch shifting method.
* “DAFX: Digital Audio Effects” by U. Z. et al.
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Pitch Shifting Algorithm
Pitch Shifting :
Steps 1) Time Stretching by a factor of α using window of size N and stepsize Sa
Input Audio
N
X1(n)
SaX2(n)
α*Sa
Step 2) Re-sampling by a factor of 1/α to change pitch
X2(n) X2(n)Km
Max correlation to preserve formant
Mixing
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Face Detection and Blockingcamera
FaceDetection
FaceDetection
Face detection is based on Viola & Jones 2001.
FaceTracking
FaceTracking
SubjectSelection
SubjectSelection
SelectiveBlocking
SelectiveBlocking
Audio segmentationresults
Subjecttalking
Producertalking
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Experimental Results
Three types of experiments
• Analysis of Segmentation algorithm
• Analysis of Audio distortion algorithm
1) Accuracy in hiding identity
2) Usability after distortion
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Segmentation ExperimentExperimental Data:
• Interview Scenario in quite meeting room
• Three interviews recording of about 1 minute and 30 seconds long
Transitions
P S P S P PS Silence
S: Subject Speaking
P: Producer Speaking
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Segmentation Results
Meeting# Transition#
(Ground truth)
Correctly identified transitions#
Falsely detected
Transitions#
Precision Recall
1 7 6 10 0.375 0.857
2 7 7 5 0.583 1
3 6 6 10 0.353 1
truthgroundin stransition#
ns transitioidentifiedcorrectly #Recall
ns transitioidentified #
ns transitioidentifiedcorrectly # Precision
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Speaker Identification Experiment
Experimental Data
• 11 Test subjects, 2 voice samples from each subject
• One voice sample is used as training and the other is used for testing
• Public domain speaker recognition software
Script1This script is used for training the speaker recognition software
Train
TestScript2This script is used to test the performance of audio distortion in hiding the identity
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Speaker Identification Results
Person ID
Without Distortion
(Person ID identified)
Distortion 1
(Person ID identified)
Distortion 2
(Person ID identified)
Distortion 3
(Person ID identified)
1 1 5 8 5
2 2 6 8 6
3 3 5 3 5
4 4 6 6 5
5 5 3 10 6
6 6 8 6 5
7 7 5 2 5
8 8 10 11 5
9 9 5 8 5
10 10 5 2 5
11 11 4 8 5
Error Rate
0% 100% 90.9% 100%
Distortion 1: (N=2048, Sa=256, α =1.5) Distortion 2: (N=2048, Sa=300, α =1.1)
Distortion 3: (N=1024, Sa=128, α =1.5)
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Usability Experiments
Experimental Data
• 8 subjects, 2 voice samples from each subject
• 1 voice is used without distortion and the other is distorted
• Manual transcription (5 human tester)
1.Wav (transcription1)1.Wav (transcription1)This transcription is of undistorted This transcription is of undistorted voice --- stored in one dot wav file.voice --- stored in one dot wav file.
2.Wav (transcription2)2.Wav (transcription2)This transcription is of distorted voice This transcription is of distorted voice sample --- in two dot wav ---.sample --- in two dot wav ---.
Manual Transcription
Unrecognized words
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Usability after distortion
Word Error Rate: Standard measure of word recognition error for speech recognition system
WER= (S+D+I) /N
S = # substitution
D = # deletion
I = # insertion
N = # words in reference sample
Tool used: NIST tool SCLITE
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Example Video
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Conclusions
Proposed Real time implementation of voice-distortion and face blocking for privacy protection in Life-log video
Analysis of audio distortion for usability Analysis of audio distortion for privacy protectionFuture Work:
Improvement in Segmentation and face blockingExpanding to the larger datasetExpanding to the noisy environment
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Acknowledgment
• People at Center of Visualization and Virtual Environment
• Department of Homeland Security
Thank you!
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√ Usefulness× Privacy
× Usefulness√ Privacy
√ Usefulness√ Privacy