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1. UnclearBallot: Automated Ballot Image Manipulation

   Matthew Bernhard, Kartikeya Kandula (October 2021, Proc. 4th International Joint Conference on Electronic Voting (E-Vote-ID ’19))

   null (Ed.)

   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. 

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2. Boardroom Voting: Verifiable Voting with Ballot Privacy Using Low-Tech Cryptography in a Single Room

   Blanchard, Nikola K.; Selker, Ted; Sherman, Alan T. (October 2020, IEEE Transactions on Systems, Man and Cybernetics: Systems, submitted)

   A boardroom election is an election that takes place in a single room — the boardroom — in which all voters can see and hear each other. We present an initial exploration of boardroom elections with ballot privacy and voter verifiability that use only “low-tech cryptography” without using computers to mark or collect ballots. Specifically, we define the problem, introduce several building blocks, and propose a new protocol that combines these blocks in novel ways. Our new building blocks include “foldable ballots” that can be rotated to hide the alignment of ballot choices with voting marks, and “visual secrets” that are easy to remember and use but hard to describe. Although closely seated participants in a boardroom election have limited privacy, the protocol ensures that no one can determine how others voted. Moreover, each voter can verify that their ballot was correctly cast, collected, and counted, without being able to prove how they voted, providing assurance against undue influence. Low-tech cryptography is useful in situations where constituents do not trust computer technology, and it avoids the complex auditing requirements of end-to-end cryptographic voting systems such as Prêt-à-Voter. This paper’s building blocks and protocol are meant to be a proof of concept that might be tested for usability and improved. 

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3. Adaptive Risk-Limiting Comparison Audits

   Harrison, Abigail; Fuller, Benjamin; Russell, Alexander (May 2023, IEEE Security and Privacy)

   Risk-limiting audits (RLAs) are rigorous statistical procedures meant to detect invalid election results. RLAs examine paper ballots cast during the election to statistically assess the possibility of a disagreement between the winner determined by the ballots and the winner reported by tabulation. The design of an RLA must balance risk against efficiency: "risk" refers to a bound on the chance that the audit fails to detect such a disagreement when one occurs; "efficiency" refers to the total effort to conduct the audit. The most efficient approaches—when measured in terms of the number of ballots that must be inspected—proceed by "ballot comparison." However, ballot comparison requires an (untrusted) declaration of the contents of each cast ballot, rather than a simple tabulation of vote totals. This "cast-vote record table" (CVR) is then spot-checked against ballots for consistency. In many practical settings, the cost of generating a suitable CVR dominates the cost of conducting the audit which has prevented widespread adoption of these sample-efficient techniques. We introduce a new RLA procedure: an "adaptive ballot comparison" audit. In this audit, a global CVR is never produced; instead, a three-stage procedure is iterated: 1) a batch is selected, 2) a CVR is produced for that batch, and 3) a ballot within the batch is sampled, inspected by auditors, and compared with the CVR. We prove that such an audit can achieve risk commensurate with standard comparison audits while generating a fraction of the CVR. We present three main contributions: (1) a formal adversarial model for RLAs; (2) definition and analysis of an adaptive audit procedure with rigorous risk limits and an associated correctness analysis accounting for the incidental errors arising in typical audits; and (3) an analysis of efficiency. 

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4. gc DLSeg: integrating graph-cut into deep learning for binary semantic segmentation

   https://doi.org/10.1364/BOE.555206  

   Xie, Hui; Xu, Weiyu; Wang, Ya_Xing; Buatti, John; Wu, Xiaodong (April 2025, Biomedical Optics Express)

   Binary semantic segmentation in computer vision is a fundamental problem. As a model-based segmentation method, the graph-cut approach was one of the most successful binary segmentation methods thanks to its global optimality guarantee of the solutions and its practical polynomial-time complexity. Recently, many deep learning (DL) based methods have been developed for this task and yielded remarkable performance, resulting in a paradigm shift in this field. To combine the strengths of both approaches, we propose in this study to integrate the graph-cut approach into a deep learning network for end-to-end learning. Unfortunately, backward propagation through the graph-cut module in the DL network is challenging due to the combinatorial nature of the graph-cut algorithm. To tackle this challenge, we propose a novel residual graph-cut loss and a quasi-residual connection, enabling the backward propagation of the gradients of the residual graph-cut loss for effective feature learning guided by the graph-cut segmentation model. In the inference phase, globally optimal segmentation is achieved with respect to the graph-cut energy defined on the optimized image features learned from DL networks. Experiments on the public AZH chronic wound data set and the pancreas cancer data set from the medical segmentation decathlon (MSD) demonstrated promising segmentation accuracy and improved robustness against adversarial attacks. 

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5. Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images.

   https://doi.org/10.1038/s41524-020-00363-x  

   Horwath, James P.; Zakharov, Dmitri N.; Mégret, Remi; Stach, Eric A. (July 2020, npj computational materials)

   null (Ed.)

   Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning meaningful image features that would enable extension to new datasets. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work, we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding, for example, the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required. With a focus on unique challenges that arise in high-resolution images, we propose methods for optimizing performance of image segmentation using convolutional neural networks, critically examining the application of complex deep learning models in favor of motivating intentional process design. 

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