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.

翻译：深度学习模型在计算机视觉任务中表现出色，但对对抗样本可能具有敏感性。本文引入Triple-Metric EvoAttack（TM-EVO），一种用于评估目标检测深度学习模型对抗攻击鲁棒性的高效算法。TM-EVO利用多度量适应度函数指导进化搜索，高效生成具有最小扰动的有效对抗测试输入。我们在广泛使用的目标检测深度学习模型DETR和Faster R-CNN以及开源数据集COCO和KITTI上评估了TM-EVO。实验结果表明，TM-EVO优于当前最先进的EvoAttack基线，能够在保持效率的同时生成噪声更少的对抗测试。