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OpenCount Improved support for Machine-Assisted Ballot-Level Audits OpenCount Improved support for Machine-Assisted Ballot-Level Audits

OpenCount Improved support for Machine-Assisted Ballot-Level Audits - PowerPoint Presentation

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OpenCount Improved support for Machine-Assisted Ballot-Level Audits - PPT Presentation

EVTWOTE 2013 Washington DC 8132013 Eric Kim Nicholas Carlini Andrew Chang George Yiu Kai Wang David Wagner University of California Berkeley University of California San ID: 816242

layout ballot style annotation ballot layout annotation style ballots blank election cont voting targets 2013 vote ballots

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Slide1

OpenCountImproved support for Machine-Assisted Ballot-Level Audits

EVT/WOTE 2013. Washington DC. 8/13/2013.

Eric

Kim, Nicholas

Carlini

, Andrew Chang, George

Yiu

,

Kai

Wang†, David

Wagner

University of California,

Berkeley

†University

of California, San

Diego

Slide2

Talk OverviewMotivationHow can

OpenCount help the audit process?ChallengesImportant: Accuracy and scalabilityPipeline Overview

Election Experiences

Questions

Slide3

What is OpenCount?Software that tabulates elections

Generates ballot-level cast vote records

CVR 00001

President of the United States

Mitt Romney

Member, County Central Com.

Shawn Nelson David John Shawver Greg Sebourn Steve Hwangbo

Slide4

MotivationWant to perform a post-election auditStatistical

ballot-level auditRisk-limiting auditTypically only have to examine tens to hundreds of ballots (depends on margin)

More efficient than alternative

CA: Each county hand-counts all ballots from1% of precincts

Slide5

Motivation (cont.)Ballot-level audits require: access to the

voting system’s interpretation of each ballotCast Vote Record (CVR) for each ballotElectronic record of the cast votes

Slide6

Motivation (cont.)Cast Vote Record (CVR)

CVR 00001

President of the United States

Mitt Romney

Member, County Central Com.

Shawn Nelson

David John Shawver Greg Sebourn Steve Hwangbo

=

?

Slide7

Motivation (cont. )Problem: current deployed voting systems do not output CVRs for each ballot

Only output election totals

Slide8

Motivation (cont. )Can’t “upgrade” existing systemsMost vendors are focusing on next-gen systems

EAC certification process (U.S. Election Assistance Commission) would make upgrade expensive

Slide9

Motivation (cont. )What is one to do?If you can’t improve it, rebuild it!

Slide10

OpenCountTabulates electionsInput: Scanned ballot images

Output: Cast Vote Records, election totals.Built specifically with ballot-level audits in mindOpen-source software (free!)

http://code.google.com/p/opencount/

Slide11

First Attempt: Blank BallotsCollect one

blank ballot from each ballot styleBlank Ballot: Unmarked ballot

Style A

Style B

Slide12

With Blank Ballots… (1/6)

Style A

Style B

Slide13

With Blank Ballots… (2/6)

Style A

Style B

Slide14

With Blank Ballots… (3/6)

Style A

Style B

Slide15

With Blank Ballots… (4/6)

Style A

Style B

Slide16

With Blank Ballots… (5/6)

Style A

Style B

Slide17

With Blank Ballots… (6/6)

Style A

Style B

Slide18

Previous WorkEVT/WOTE 2012 (Bellevue, Washington)First introduction of the

OpenCount (2012) system“Operator-Assisted Tabulation of Optical Scan Ballots”. Kai Wang, Eric Kim, Nicholas

Carlini

, Ivan

Motyashov

, Daniel Nguyen, David Wagner.

Required collecting all blank ballots

Slide19

Previous Work (cont.)Problem: Did not scale to large elections

Collecting blank ballots is a huge burden for election officialsBlocked some counties from participatingOverall, too much required effort

Slide20

A Second AttemptNew approach: No blank ballots

Slide21

How can we do this?

Slide22

No Blank Ballots

Style A

Style B

Slide23

No Blank Ballots

How to find:

Voting Targets?

Contests?

Slide24

OpenCount PipelineOverview of system

Election experiencesCalifornia risk-limiting audit pilot program

Slide25

Scan Ballots (1/6)Use any commercial, off-the-shelf scanner

Slide26

Ballot Grouping (2/6)

Slide27

Ballot Grouping (2/6)

Slide28

Ballot Grouping (2/6)

Slide29

Ballot Grouping (2/6)

~124,000 Ballots

~200 Styles

Slide30

Ballot Grouping (2/6)

Slide31

Ballot Grouping (2/6)

Slide32

Ballot Grouping (2/6)Implemented vendor-specific barcode decodersDiebold

ES&SHartSequoia

Slide33

Layout Annotation (3/6)GoalSpecify location of contests and voting targets

Perform data entry of contest textOnly need to annotate one ballot from each style

Slide34

Layout Annotation (3/6)

How to find voting targets automatically?

Slide35

Layout Annotation (3/6)

1.) User selects empty voting target

Slide36

Layout Annotation (3/6)

1.) User selects empty voting target

Slide37

Layout Annotation (3/6)Search for empty voting target on ballotsTemplate Matching

Grid-searchSearch for this:

Slide38

Layout Annotation (3/6)

Verify Matches

Slide39

Layout Annotation (3/6)

Problem

: Voter marks interfere with template matching

Slide40

Layout Annotation (3/6)

Problem

: Voter marks interfere with template matching

Idea

: Voters vote differently. Can find missing targets on other ballots with the same style

Slide41

Layout Annotation (3/6)

Ballot A

Idea

: Voters vote differently. Can find missing targets on other ballots with the same style

Slide42

Layout Annotation (3/6)

Ballot B

Idea

: Voters vote differently. Can find missing targets on other ballots with the same style

Slide43

Layout Annotation (3/6)

Union of detections from A + B

Idea

: Voters vote differently. Can find missing targets on other ballots with the same style

Slide44

Layout Annotation (3.5/6)Contest text data entryContest title, candidate names

Judge of the Superior Court (Office No. 1)

Deborah J. Chuang

Eugene

Jizhak

Slide45

Layout Annotation (3.5/6)Can’t rely completely on OCRManually labeling each contest takes forever

Number of distinct contests is smallA few hundred at mostContests are duplicated on many ballot styles“President of the US”

Slide46

Layout Annotation (3.5/6)Should only have to label this contest once!

Slide47

Layout Annotation (3.5/6)Want to detect contest duplicatesSimple idea: compare contest images

Pixel-difference (L2 norm)

Slide48

Layout Annotation (3.5/6)

-

Diff = 0.058

MATCH

Slide49

Layout Annotation (3.5/6)

-

Diff = 0.175

NOT MATCH

Slide50

Layout Annotation (3.5/6)Problem: contest visual appearance variesWord spacing, line wrapping, candidate re-ordering

Different Line Wrap

Slide51

Layout Annotation (3.5/6)

-

Diff = 0.146

NOT MATCH

Slide52

Layout Annotation (3.5/6)Our approach: utilize OCR + edit-distance

Slide53

Layout Annotation (3.5/6)Our approach: utilize OCR + edit-distance

Slide54

Layout Annotation (3.5/6)Our approach: utilize OCR + edit-distance

Slide55

Layout Annotation (3.5/6)Our approach: utilize OCR + edit-distance

Match!

Slide56

Ballot Interpretation (4/6)GoalDetermine if voting targets are “filled” or “empty”

Filled

Empty

Slide57

Ballot Interpretation (4/6)

Separating Line

Sorted by Average Pixel Intensity

Slide58

Ballot Interpretation (4/6)

Slide59

Ballot Interpretation (4/6)

Slide60

Ballot Interpretation (4/6)

Slide61

Ballot Interpretation (4/6)

Filled

Empty

Slide62

Generate CVRs (5/6)Output CVRs

Slide63

Perform Audit (6/6)Finally, perform the audit!Done!

Slide64

Election ExperiencesOpenCount has been used to support risk limiting pilot audits in several California counties

Alameda, Madera, Merced, Napa, San Luis Obispo, Stanislaus, VenturaOpenCount’s results matched all

examined paper ballots

perfectly

Slide65

Election Experiences (cont.)

County

#

Ballots

# Ballot Styles

Total Time (2013)

Stanislaus

3,151

17m 18sMerced7,120112m 31s

Ventura

17,301

1

23m 6s

Alameda

1,374

8

22m 1s

San Luis Obispo

10,689

27

30m 35s

Madera

3,757

1

6m

38s

Napa

6,809

11

1h 56m 9s

Yolo

35,532

623

3h 36m

Slide66

Election Experiences (cont.)

County

#

Ballots

# Ballot Styles

Total Time (2013)

Speedup

(2012 / 2013)

Stanislaus3,15117m 18s2.40xMerced

7,120

1

12m 31s

2.04x

Ventura

17,301

1

23m 6s

2.52x

Alameda

1,374

8

22m 1s

1.29x

San Luis Obispo

10,689

27

30m 35s

2.78x

Madera

3,757

1

6m

38s

1.28x

Napa

6,809

11

1h 56m 9s

2.78x

Yolo

35,532

623

3h 36m

16.24x

Slide67

Election Experiences (cont.)

County

#

Ballots

# Ballot Styles

Total Time (2013)

Speedup

(2012 / 2013)

Stanislaus3,15117m 18s2.40xMerced

7,120

1

12m 31s

2.04x

Ventura

17,301

1

23m 6s

2.52x

Alameda

1,374

8

22m 1s

1.29x

San Luis Obispo

10,689

27

30m 35s

2.78x

Madera

3,757

1

6m

38s

1.28x

Napa

6,809

11

1h 56m 9s

2.78x

Yolo

35,532

623

3h 36m

16.24x

Slide68

Election Experiences (cont.)

County

#

Ballots

# Ballot Styles

Total Time (2013)

Human

Time

Marin29,12139811h 53m5h 45mSanta Cruz34,004

136

18h 50m

5h 27m

Leon

124,200

216

14h 2s

1h

53m

Orange

294,402

1,839

3d 22h 39s

1d 8h 25m

Previous version (2012) could not process elections of this size and complexity.

Progress!

Slide69

ConclusionImprovements to the OpenCount system

Don’t have to collect blank ballotsReduce operator effort significantlyOpenCount is ready for election officials to use

Used in ballot-level risk-limiting audits

Audits

m

ade possible by

OpenCount