UnclearBallot: Automated Ballot Image Manipulation

As paper ballots and post-election audits gain increased adoption in the United States, election technology vendors are offering products that allow jurisdictions to review ballot images—digital scans produced by optical-scan voting machines—in their post-election audit procedures. Jurisdictions including the state of Maryland rely on such image audits as an alternative to inspecting the physical paper ballots. We show that image audits can be reliably defeated by an attacker who can run malicious code on the voting machines or election management system. Using computer vision techniques, we develop an algorithm that automatically and seamlessly manipulates ballot images, moving voters’ marks so that they appear to be votes for the attacker’s preferred candidate. Our implementation is compatible with many widely used ballot styles, and we show that it is effective using a large corpus of ballot images from a real election. We also show that the attack can be delivered in the form of a malicious Windows scanner driver, which we test with a scanner that has been certified for use in vote tabulation by the U.S. Election Assistance Commission. These results demonstrate that post-election audits must inspect physical ballots, not merely ballot images, if they are to strongly defend against computer-based attacks on widely used voting systems.