OpenScan: A Fully Transparent Optical Scan Voting System

Existing optical scan voting systems depend on the integrity of the scanner. If a compromised--or merely faulty--scanner reports incorrect results, there is no ready mechanism for detecting errors. While methods exist for ameliorating these risks, none of them are entirely satisfactory. We propose an alternative: a radically open system in which any observer can simultaneously and independently count the ballots for himself. Our approach, called OpenScan, combines digital video recordings of ballot sheet feeding with computer vision techniques to allow any observer with a video camera to obtain a series of ballot images that he can then process with ordinary optical scan counting software. Preliminary experimental results indicate that OpenScan produces accurate results at a manageable cost of around $1000 in hardware plus $0.0010 per ballot counted.

[1]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[2]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[3]  Kenneth C. Johnson.  Election Certification by Statistical Audit of Voter-Verified Paper Ballots , 2004 .

[4]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[5]  Josh Benaloh,et al.  Simple Verifiable Elections , 2006, EVT.

[6]  Yakup Genc,et al.  GPU-based Video Feature Tracking And Matching , 2006 .

[7]  D. Dill,et al.  The Role of Dice in Election Audits – Extended Abstract , 2006 .

[8]  Lyndsey C. Pickup,et al.  Machine learning in multi-frame image super-resolution , 2007 .

[9]  Anwar Adi.  Demonstration of “ Open Counting ” : A Paper-Assisted Voting System with Public OMR-AtA-Distance Counting , 2007 .

[10]  Joseph A. Calandrino.  Machine-Assisted Election Auditing , 2007, EVT.

[11]  Qi Zhang,et al.  SIFT implementation and optimization for multi-core systems , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[12]  Edward W. Felten,et al.  In Defense of Pseudorandom Sample Selection , 2008, EVT.

[13]  Hovav Shacham,et al.  You Go to Elections with the Voting System You Have: Stop-Gap Mitigations for Deployed Voting Systems , 2008, EVT.

[14]  Hao Feng,et al.  Parallelization and characterization of SIFT on multi-core systems , 2008, 2008 IEEE International Symposium on Workload Characterization.

[15]  M. Lindeman,et al.  Principles and Best Practices for Post-Election Audits , 2008 .

[16]  Jeremy Clark,et al.  Scantegrity II: End-to-End Verifiability for Optical Scan Election Systems using Invisible Ink Confirmation Codes , 2008, EVT.

[17]  David A. Wagner,et al.  Weight, Weight, Don't Tell Me: Using Scales to Select Ballots for Auditing , 2009, EVT/WOTE.

[18]  Philip B. Stark.  Efficient Post-Election Audits of Multiple Contests: 2009 California Tests , 2009 .

[19]  Eric Rescorla.  Understanding the Security Properties of Ballot-Based Verification Techniques , 2009, EVT/WOTE.

[20]  Daniel P. Lopresti,et al.  Camera-Based Ballot Counter , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[21]  Philip B. Stark,et al.  Risk-Limiting Postelection Audits: Conservative $P$-Values From Common Probability Inequalities , 2009, IEEE Transactions on Information Forensics and Security.