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Lagron Roman-Seminar On Vision Based Security 1
Blinkering Surveillance:Enabling Video Privacy through
Computer Vision
Andrew Senior,Sharath Pankanti,Arun Hampapur,Lisa Brown,Ying-Li Tian,Ahmet Ekin
Lagron Roman-Seminar On Vision Based Security 2
Abstract
Review of privacy in video surveillance
Using a computer vision approach to understanding the video can be used to hide superfluous details, particularly identity.
Lagron Roman-Seminar On Vision Based Security 3
Introduction
In recent years we have seen a world-wide rise in the use of Closed-Circuit Television (CCTV) cameras.Now we see rise in video processing systems:
Can interpret the videoExtract usable data: movements, identities, events.
Lagron Roman-Seminar On Vision Based Security 4
Introduction-cont.
Video surveillance can readily be used as a tool for statecontrol and oppression, to spy on people and for voyeurism.
Similar algorithms can be used to filter that same video,altering it and restricting the amount of privacy-intrusivedata contained in the video, while preserving enoughinformation to be useful for the original task.
http://www.youtube.com/watch?v=JqcEUdboN2o
Lagron Roman-Seminar On Vision Based Security 5
Rise of video surveillance Video cameras are being installed in urban areas throughout thedeveloped world- principally as a deterrent to crime.
- Only 4% reduction in crime. - CCTV moving crime out of the camera
boundaries. - Unmonitored zones become targets for
illegal activity- Criminal acts committed in a private
location, such as a locker room or restroom
- Helps to Solve crimes. - Areas under surveillance become crime-free.
http://www.youtube.com/watch?v=BLcmaITB_Lc http://www.youtube.com/watch?v=gcE106AMOWA
Lagron Roman-Seminar On Vision Based Security 6
Rise of video surveillance-cont.
Reasons for Surveillance is spreading:
Storage costs ↓
Installation costs ↓
Technology
↑ Production quantities ↑
Prices of video cameras
↓
Prices of saving the video
↓Prices of multiple
cameras installation
↓
Lagron Roman-Seminar On Vision Based Security 7
Public concerns
Recent technological developments and the threat of
blanket video surveillance have heightened publicconcern about the less benign effects of mass
surveillance“Big Brother" could know their every act and inspire the self
censorship intended by the Panopticon: (George Orwell, “1984”):
“So long as he remained within the field of vision which the metal plaque commanded, he could be seen as well as heard. There was of course no way of knowing whether you were being watched at any given moment”.( http://www.online-literature.com/orwell/1984/)
Lagron Roman-Seminar On Vision Based Security 8
Public concerns – cont.The ACLU (American Civil Liberties Union) has
outlineda number of concerns about video surveillance,describing five abuses of CCTV:
1. Criminal abuse
2. Institutional abuse
3. Abuse for personal purposes
4. Discriminatory targeting
5. Voyeurism
http://www.researchchannel.org/prog/displayevent.aspx?rID=5036&fID=345#
http://www.youtube.com/watch?v=DHarMDHRhC4
Lagron Roman-Seminar On Vision Based Security 9
Automated surveillance
CCTV systems have already publicly deployed face recognition software → the potential for identifying and tracking people.
Systems gather much richer information about the people being observed.
Beginning to make judgments about their actions and behaviors.
Lagron Roman-Seminar On Vision Based Security 10
Automated surveillance-cont.
Algorithms bring the potential to automatically track individuals across multiple cameras.
Systems tracking a particular person throughout the day: showing what happens at a particular time of day looking for people or vehicles that return to a locationAlgorithms exist for tracking people, understanding their interactions.
Compression algorithms have reduced the storage needs.
Lagron Roman-Seminar On Vision Based Security 11
CCTV cameras help Parking Services team to enforce both parking and traffic
contraventions.
Central London Congestion Charging scheme : it was first introduced in
February 2003 to discourage traffic congestion in central London
Automated surveillance-Example
http://www.youtube.com/watch?v=LQ0JMAV2DGg
Lagron Roman-Seminar On Vision Based Security 12
What is a general data privacy ?
Privacy means different things to differentpeople - what is considered acceptable orintrusive is a result of cultural but alsotechnological capability.
In the United Kingdom principles of data protection
(Data Protection Act-DPA) saying that data must be:
fairly and lawfully processedprocessed for limited purposesadequate, relevant and not excessiveaccuratenot kept longer than necessaryprocessed in accordance with the data subject's rightssecurenot transferred to countries without adequate protection
Lagron Roman-Seminar On Vision Based Security 13
Why video is different?
Difficulty in processing it automatically to extract useful information.
Harder to assess the privacy.
It takes time to review video to find “interesting” excerpts.
Even in a liberal democracy and with many checks and balances, the potential for abuse is large.
Laws which are rarely enforced end up being applied selectively and unfairly
Lagron Roman-Seminar On Vision Based Security 14
Technological video privacy Sony-system that detects skin tone and replaces it with another color.
Matsushita-system for obscuring a “privacy region” being observed by a pan-tilt-zoom camera.
Newton-a system for “de-identifying” faces by transforming faces in shared surveillance video
Cluster memberCluster memberDe-identified
http://privacy.cs.cmu.edu/people/sweeney/video.html
Lagron Roman-Seminar On Vision Based Security 15
A model for video privacy
Parameter Solution
What data is present
Consent
Who sees the data
How long is the data kept
How raw is the data
What form the data is in
Limit the data capture. Blinkers, blinds. Lens caps, indicators of when the camera is in operation, a low resolution camera, defocused lens.
Use signs to inform the public
Key is required to access the data. Access control rules. Playback, searching, freeze frame etc require different levels of authorization.
Minimize the data lifetime.
The crucial aspect for privacy. Mask out privacy-invasive features
Data should be stored digitally and encrypted. Encryption should be carried out at the camera
Lagron Roman-Seminar On Vision Based Security 16
Absolute versus relative ID A major distinction among video surveillance systems is
the level ofanonymity they afford.
Relative ID: Systems can recognize people they have seen before,
buthave no enrollment step. Can be used to collect
statisticsabout people's comings and goings, but do not know
anyindividual information. Use weaker methods ofidentification. Collect short term statistics. Unable torecognize people over periods of time longer than a
day.
Anonymous: Knows nothing about the individuals that are
recordedonto the tape or presented on the monitors. While
opento abuse by individuals watching the video, it does
notFacilitate that abuse.
Absolute ID: Have some method of identifying the individuals
observed(face recognition, badge swipe correlated with the
video)and associating them with a personal record in a
database.Require some kind of enrollment process to register
theperson in the database.
Lagron Roman-Seminar On Vision Based Security 17
Privacy preserving video console
“Privacy Cam”- A camera with onboard processing. Produces a video stream with the privacy-intrusive information already removed.
Prototype system to record and redistribute surveillance video.Designed to minimize the intrusion.It concentrates on what data is present and How raw is the data issues.Re-renders the video stream to hide the privacy-intrusive details.Preserving the information necessary for the system to be useful.
Lagron Roman-Seminar On Vision Based Security 18
System architecture
Selective video decoding system-operates under the control of a user authentication system.
Encoding system - consists of video analysis, transformation, and encryption subsystems
Lagron Roman-Seminar On Vision Based Security 19
Enrollmentdatabase
Authentication
Authentication module
The encoding system
Video sourceAnalysis &Informationextraction
Transformation Encoding
Transformationparameters
Keygeneration
Lagron Roman-Seminar On Vision Based Security 20
Analysis subsystem
Takes a stream and analyzes thevideo at more sophisticated
levels toextract separate streams ofinformation:
1.Appearance of background2.Extracting objects of
interest3.Separating people from
vehicles4.Distinguishing people who
walk in groups5. Distinguishing betweendifferent limbs within a
person
Video source
Backgroundsubtraction
Backgroundestimation
Connectedcomponent
analysisTracking
Object tracks
Enrollmentdatabase
Objectidentification
Authentication module
Objectclassification
Object class
Partidentification
Parttracking
Activity
Lagron Roman-Seminar On Vision Based Security 21
Analysis subsystem-cont. Generic object detection approach - all objects of interest are
dened interms of one or more attributes (features).For example - moving
objects.In a model specific approach, each object of interest is modelled andexplicitly detected using model-based techniques. For example-
humans andvehicles.
Lagron Roman-Seminar On Vision Based Security 22
Transformation subsystem
The transformation subsystem selectively transforms the
information extracted from the video based on the system
policy.
System policies may choose to partially/fully obscure orstatistically perturb one or more components of
extractedinformation such as location, pose, activity,track, and
so on.
The transformed information components constitute an
encoded video channel.
Lagron Roman-Seminar On Vision Based Security 23
The encryption subsystem
Extracted information is encrypted using the encryption
subsystem, using different keys for different information
streams.
The encoded video may contain multiple copies of essentially
the same information,although each of the copies may be
encoded with a different key.
The encoded video is modular in nature and each module (or
channel) represents one or more components of extracted or
raw video information.
Lagron Roman-Seminar On Vision Based Security 24
Secure encryptionWhat is a definition of secure
encryption ?• Key length?• Computation time?
The adversary sees the same distribution of ciphertext, regardless of the message sent. (perfect indistinguishability)
1 2Pr[ ( ) ] Pr[ ( ) ]
k kc cm mE E
1 2,
P plain text
E encryption scheme
k encryption key
two messages of the same lengthm m
Lagron Roman-Seminar On Vision Based Security 25
Secure encryption
Pr [ | ( ) ] Pr[ ]k
M m M c M mE
After seeing the ciphertext, the adversary doesn't know more about the message thanbefore seeing the ciphertext.
The a posteriori knowledge is the same as the a priori knowledge.
We think of M as known to the adversary.
Lagron Roman-Seminar On Vision Based Security 26
A (private-key) encryption scheme consists of three algorithms (G,E,D), as follows:
Symmetric (Private Key) Encryption
The encryption algorithm E is a randomized algorithm that takes a key and a plaintext and outputs a ciphertext
k K m Pc C
The decryption algorithm D is a deterministic algorithm that takes a key and a ciphertext and returns a plaintext .
k Km P
c C
Examples: DES, AES
The key generation algorithm G is a randomized algorithmthat returns a key
k K
Lagron Roman-Seminar On Vision Based Security 27
Public Key EncryptionA public-key encryption scheme consists of three polynomial-time algorithms (G,E,D), as follows:
The key generation algorithm G is a randomized algorithm that takes a security parameter as input returns a pair (pk; sk), where pk is the public key and sk is the secret key.
1n
The encryption algorithm E is a stateless randomized algorithm that takes the public key pk and a plaintext m and outputs a ciphertext c
The decryption algorithm D is a deterministic algorithm that takes the secret key sk and a ciphertext c and returns a plaintext .( )
skm cD
Examples: RSA
Lagron Roman-Seminar On Vision Based Security 28
The decoding system
Encodedvideo
Statisticalquery
processor
Enrollmentdatabase
Authentication
Authentication module
Keygeneration &authorization
Selectivedecryption
Decodedraw video
Transformedvideo
synthesis
Query
Query
outputA
uth
ori
zati
on
Query
output
Lagron Roman-Seminar On Vision Based Security 29
Granting access Layered approach - the different kinds of data extracted by the
system.
Video
Rendering
hide identit
y
hide times
alert on
event
hide actions
hide locatio
ns
Statistics
How many
people
Alert me if X
shows up
Average flow patter
n
Ordinary users access statistics
Law enforcement
access video on emergency or
court order
Privileged users access more information
Lagron Roman-Seminar On Vision Based Security 30
The PrivacyCam
Output is in the form of a re-rendered NTSC video stream.
On-board processing power. The video encoding,transformationand encryption take place on the camera before transmission.
Re-rendered output video stream, encrypted informationstreams can also be transmitted via other output ports, such
asover a wireless network.
http://cs.uccs.edu/~cs591/studentproj/projS2007/achattop/doc/Present.ppt
http://www.youtube.com/watch?v=vJkBCfPBzAU
Lagron Roman-Seminar On Vision Based Security 31
Guaranteeing video privacy Perfect performance can not be guaranteed
* Missed detection * False alarm. * Selecting the appropriate system operating point.
Single missed detection may
reveal personal information
over extended periods of time.
Occasional false alarm may have a
limited impact on the effectiveness
of the installation.
Even with perfect detection, anonymity cannot beguaranteed. Contextual information may be enough
touniquely identify a person.
Lagron Roman-Seminar On Vision Based Security 32
Increasing public acceptance
Q: Why anybody would accept this extra burden? A: In the future, it may be required by law that CCTV
systems impose privacy protection of the form that we describe.
Q: What guarantee a citizen has that a claimed privacy protection is actually in force?
A: A potential solution is certification and registration of systems
Lagron Roman-Seminar On Vision Based Security 33
Conclusions
Video surveillance and person-aware video systems are here to stay and will grow ever more powerful.We have presented a model for future systems that take a technological approach to defending video privacy.