Protecting privacy from CCTV

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Grant Leonard
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1 Protecting privacy from CCTV Ori Brostovski March 2, 2008

2 Introduction In this presentation I am going to talk about: The abuse potential of CCTV-s. The PrivacyCam model that was offered in Enabling Video Privacy Through Computer Vision. An outline of the irregularity detection algorithm offered in Detecting Irregularities in Images and in Video, this algorithm can be used by the PrivacyCam.

3 The PrivacyCam

4 The Problem CCTV cameras are widely used, recording videos of most people. The ability to use computer vision to detect a specific person makes abuse of these videos too convenient.

5 Abuses of CCTV The American Civil Liberties Union has defined possible abuses of CCTV: Criminal abuse. Abuse for personal purposes. Discriminatory targeting. Voyeurism.

6 Legitimate uses of CCTV It should be noted that CCTV-s has many legitimate uses: Security - By stopping crimes from occurring in real-time. Crime prevention - By being able to identify the criminal after the crime has occurred Automated buildings - Allows heating/lighting/elevators/phones to automatically adjust to people within the building.

7 Preventing abuse by limiting access In order to be able to protect data from abuse, we need to think about the following limitations: What data can be obtained from the video? Who can see the data? How long are the data kept? (Personally I do not feel that there is really a way to check this) What meta-data are attached to the data? (i.e. time, place, etc.) Is the data stored safely?

8 A solution - The PrivacyCam A PrivacyCam system with specific properties is offered as a solution to the problems posed previously. The PrivacyCam will be composed of: PrivacyCam - A camera that sends pictures to a privacy console. The PrivacyCam may also do visual analysis on the videos. Privacy Console - users can get access from it videos or meta-data that have been modified to ensure anonymity.

9 Encryption The PrivacyCam should work by sending encrypted videos to a privacy console. The purpose of such an encryption is to prevent a user from tapping into the camera s communication channel with the console in order to get raw video information.

10 Video analysis The privacy console should analyze the video, extract privacy related information from it and create copies with different parts of the removed information and encrypting each copy with a different key.

11 Permission hierarchy The PrivacyCam will implement a hierarchy of permission to allow different users access to different parts of the video and/or metadata. There may be many users, possibly including anonymous ones or devices in the building.

12 Logging All information queries to the privacy console should be securely logged. The logging acts as a deterrent for potential abusers of the PrivacyCam.

13 Object recognition can miss There are a few problems with the PrivacyCam. Object recognition can sometimes fail to identify an object that should be hidden. This can be solved partially by defining an operating point. The operating point is a threshold that specifies to recognition algorithm how interesting something needs to be in order for it to be hidden by the PrivacyCam. Higher authority gets access to copies with a lower operating point.

14 Lack of knowledge about the adversary Even if object recognition and filtering is perfect, there is still the problem of not knowing what the adversary knows. Furthermore, the adversary of tomorrow may have access to different video analysis technologies than the PrivacyCam of today, which will allow him better data extraction ability than anticipated today.

15 Logs may give out information If many people can see the logs than this allows for a state where information can be harvested based on watching who watched what. If few people can see the logs than corruption becomes easier.

16 Summary In this presentation we have: We have described the possible abuse of CCTV cameras. We have described a possible solution to prevent such abuse. We have shown that while the ideas behind the solution are good, there are several problems in it.

17 Detecting Irregularities

18 The setting Let us limit our setting to security-related CCTV-s. Let database be some database of regular behaviors. Let query be a video we want to check. We show an algorithm that can detect irregular video events in a query, based on the regular video events in database.

19 Definition of videos For our purposes, we will think of videos as a three dimensional matrix with it s dimensions corresponding to width, height and time accordingly.

20 Definition of descriptors A descriptor represents a specialized way of looking at a specific part of the image. Similar images should have similar descriptors.

21 The temporal derivative descriptor Let Y, X, T be coordinates of row, column and time accordingly. We define D [i][j][k] be the temporal derivative of a video pixel at [Y + i][x + j][t + k]. We define D := D D, D is the temporal derivative descriptor. The intuition behind D is that it describes the motion at it s 7 7 patch.

22 Partition to regions We can partition the video to overlapping regions. Each query region will get a likelihood score, which says how much it s similar to a region to a data region.

23 Partition of regions to patches Each region is partitioned to patches q 1,..., q n. Each patch has a position vector l 1 y,..., l n y and a descriptor value d 1 y,..., d n y. Each region has an origin position c y.

24 Database regions The set database contains regions in the same manner as the query regions. We will use a similar notation for them, but replace y with x (i.e. positions are l 1 x,... ) We will refer to the database patches as r 1, r 2,....

25 Similarity score of two patches Given a database patch r j and a query patch q i. We can define the likelihood between the two as: P(r j, q i ) = e ( (l j x c x ) (l i y c y ) 2) e ( dj x d i y 2 )

26 Likelihood of a query patch We can define this by finding the database patch that resembles the query patch the most: mlc i (c x) = max P(r j, q i ) j Note that c x is given as an input since we move the database patch a bit inside the query region.

27 Likelihood of a query region We simply propagate the likelihood values from patches: m c (c x ) = i m i lc (c x)

28 Likelihood score of a pixel We define the likelihood of a pixel as the maximum of it s likelihoods in each of the regions that contains it. Occlusion may cause us to get low likelihood values and requires special treatment.

29 Using the likelihood score Using a simple threshold, one can use now check the likelihood score in order to find out suspicious movements. This can be used in the PrivacyCam in a way such that parts of the video with suspicious movements are accessible to more people than the rest of the video.

30 Bibliography A. Senior, S. Pankanti, A. Hampapur, L. Brown, Y. Tian, A. Ekin. Enabling Video Privacy Through Computer Vision. O. Boiman and M. Irani, Detecting Irregularities in Images and in Video

Video Privacy in the Age of Automated Surveillance

Video Privacy in the Age of Automated Surveillance Lisa M. Brown, Senem Velipasalar, Andrew W. Senior, Sharath Pankanti, Arun Hampapur, Chiao-fe Shu, Ying-li Tian IBM T.J Watson Research Center Yorktown