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University of Michigan-Dearborn

Academic institutionnorthamerica · us
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Research library41linked papers
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Selected work

Representative Papers

TaintRadar: Semantic-Aware Taint-Style Vulnerability Detection via Augmented Code Property Graphs

Jul 17, 2026

Static taint analysis often suffers from high false positives or false negatives due to coarse-grained sanitization modeling, blind spots in database propagation, and insufficient handling of object-level data flows. This work proposes an enhanced code property graph approach that jointly models sanitization logic, persistent propagation, and object field–level name semantics for the first time. It introduces vulnerability-type-aware sanitization analysis, a database-state-aware cross-script propagation mechanism, and object-aware reaching definitions, further integrating context-sensitive parameter binding, database schema constraints, and query safety verification. Evaluated on the SARD benchmark, the method maintains 80% accuracy while significantly reducing false positives. In experiments on 19 real-world PHP applications, it successfully reproduces most known CVEs and uncovers 29 previously undisclosed zero-day vulnerabilities—26 SQL injections and 3 stored XSS flaws.

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Mind the Privileged-to-Camera Gap: Actor-Centric Sidecar Supervision for Camera-First Open-Loop Waypoint Prediction

Jun 18, 2026

This work addresses the representational gap between perception and behavior prediction in purely vision-based autonomous driving models, which arises from the absence of explicit role-level supervision for surrounding road users when trained solely with waypoint supervision. To bridge this gap, the authors propose a role-centric “sidecar” supervision mechanism that leverages simulator-generated labels—including road user positions, post-hoc relevance annotations, and short-term motion cues—to enhance the waypoint prediction capability of a model fusing multi-view RGB images, ego-vehicle states, and route commands during training. Notably, this approach requires no additional inputs at inference time and substantially narrows the performance gap between vision-only and privileged-information methods. Evaluated in open-loop trajectory prediction, the method achieves a final displacement error (FDE) of 1.223 ± 0.01 meters, a 32.6% improvement over the baseline, with particularly pronounced gains in multi-agent interactions and scenarios involving vulnerable road users.

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An Open-Source Two-Stage Computer Vision Pipeline for Fine-Grained Vehicle Classification using Vision Transformers

Jun 03, 2026

Existing open-source tools struggle to automatically perform fine-grained classification of vehicles by crash injury risk from naturalistic road videos. This work proposes the first open-source vision system tailored for cycling safety: in the first stage, it employs RT-DETR for vehicle detection, followed by a second stage that uses a fine-tuned ViT-Base/16 model to classify vehicles into six body types with fine-grained granularity. To mitigate unreliable predictions, the system incorporates a softmax-confidence-based abstention mechanism. Evaluated on an in-house dataset, the system achieves 94% accuracy (F1: 0.91–0.97); notably, it maintains 89% accuracy on unseen external data, with most categories achieving F1 scores of at least 0.90, demonstrating strong cross-domain robustness and uncertainty-aware prediction capability.

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Recent publications

Latest Papers

TaintRadar: Semantic-Aware Taint-Style Vulnerability Detection via Augmented Code Property Graphs

Jul 17, 2026

Static taint analysis often suffers from high false positives or false negatives due to coarse-grained sanitization modeling, blind spots in database propagation, and insufficient handling of object-level data flows. This work proposes an enhanced code property graph approach that jointly models sanitization logic, persistent propagation, and object field–level name semantics for the first time. It introduces vulnerability-type-aware sanitization analysis, a database-state-aware cross-script propagation mechanism, and object-aware reaching definitions, further integrating context-sensitive parameter binding, database schema constraints, and query safety verification. Evaluated on the SARD benchmark, the method maintains 80% accuracy while significantly reducing false positives. In experiments on 19 real-world PHP applications, it successfully reproduces most known CVEs and uncovers 29 previously undisclosed zero-day vulnerabilities—26 SQL injections and 3 stored XSS flaws.

0 citationsRead paper

Mind the Privileged-to-Camera Gap: Actor-Centric Sidecar Supervision for Camera-First Open-Loop Waypoint Prediction

Jun 18, 2026

This work addresses the representational gap between perception and behavior prediction in purely vision-based autonomous driving models, which arises from the absence of explicit role-level supervision for surrounding road users when trained solely with waypoint supervision. To bridge this gap, the authors propose a role-centric “sidecar” supervision mechanism that leverages simulator-generated labels—including road user positions, post-hoc relevance annotations, and short-term motion cues—to enhance the waypoint prediction capability of a model fusing multi-view RGB images, ego-vehicle states, and route commands during training. Notably, this approach requires no additional inputs at inference time and substantially narrows the performance gap between vision-only and privileged-information methods. Evaluated in open-loop trajectory prediction, the method achieves a final displacement error (FDE) of 1.223 ± 0.01 meters, a 32.6% improvement over the baseline, with particularly pronounced gains in multi-agent interactions and scenarios involving vulnerable road users.

0 citationsRead paper

An Open-Source Two-Stage Computer Vision Pipeline for Fine-Grained Vehicle Classification using Vision Transformers

Jun 03, 2026

Existing open-source tools struggle to automatically perform fine-grained classification of vehicles by crash injury risk from naturalistic road videos. This work proposes the first open-source vision system tailored for cycling safety: in the first stage, it employs RT-DETR for vehicle detection, followed by a second stage that uses a fine-tuned ViT-Base/16 model to classify vehicles into six body types with fine-grained granularity. To mitigate unreliable predictions, the system incorporates a softmax-confidence-based abstention mechanism. Evaluated on an in-house dataset, the system achieves 94% accuracy (F1: 0.91–0.97); notably, it maintains 89% accuracy on unseen external data, with most categories achieving F1 scores of at least 0.90, demonstrating strong cross-domain robustness and uncertainty-aware prediction capability.

0 citationsRead paper