Generating Minimalist Adversarial Perturbations to Test Object-Detection Models: An Adaptive Multi-Metric Evolutionary Search Approach
<|reference_start|>Generating Minimalist Adversarial Perturbations to Test Object-Detection Models: An Adaptive Multi-Metric Evolutionary Search Approach: 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 object-detection 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.<|reference_end|>
@article{mcintyre-garcia2024generating,
    title={Generating Minimalist Adversarial Perturbations to Test Object-Detection
  Models: An Adaptive Multi-Metric Evolutionary Search Approach},
    author={Cristopher McIntyre-Garcia, Adrien Heymans, Beril Borali, Won-Sook Lee
  and Shiva Nejati},
    journal={arXiv preprint arXiv:2404.17020},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17020},
    primaryClass={cs.SE cs.AI}
}