ICST 2024
Mon 27 - Fri 31 May 2024 Canada

This program is tentative and subject to change.

Tue 28 May 2024 10:15 - 10:30 at Room 1 - Session 1 (Opening, Keynote, Papers 1)

Deep Learning (DL) models excel in computer vision tasks but can be susceptible to adversarial examples. This paper introduces Triple-Metric EvoAttack (TM-EVO), an efficient algorithm for evaluating the robustness of object-detection DL models against adversarial attacks. TM-EVO utilizes a multi-metric fitness function to guide an evolutionary search efficiently in creating effective adversarial test inputs with minimal perturbations. We evaluate TM-EVO on widely-used objectdetection DL models, DETR and Faster R-CNN, and open-source datasets, COCO and KITTI. Our findings reveal that TM-EVO outperforms the state-of-the-art EvoAttack baseline, leading to adversarial tests with less noise while maintaining efficiency.

This program is tentative and subject to change.

Tue 28 May

Displayed time zone: Eastern Time (US & Canada) change

09:00 - 10:30
Session 1 (Opening, Keynote, Papers 1)AIST at Room 1
Day opening
Workshop Opening
Gregory Gay Chalmers | University of Gothenburg, Sebastiano Panichella Zurich University of Applied Sciences, Aitor Arrieta Mondragon University
Towards Better Software Quality in the Era of Large Language Models
Lingming Zhang University of Illinois at Urbana-Champaign
Generating Minimalist Adversarial Perturbations to Test Object-Detection Models: An Adaptive Multi-Metric Evolutionary Search Approach
Cristopher McIntyre-Garcia , Adrien Heymans University of Ottawa, Beril Borali University of Ottawa, Won-Sook Le University of Ottawa, Shiva Nejati University of Ottawa
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