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.

该论文介绍了一种名为Triple-Metric EvoAttack（TM-EVO）的有效算法，用于评估目标检测深度学习模型对抗性攻击的鲁棒性，通过利用多度量适应函数指导进化搜索来创建具有最小扰动的有效对抗测试输入，评估结果表明，TM-EVO优于基准算法EvoAttack，能够生成更少噪音且保持高效的对抗测试。

生成极简对抗扰动以测试物体检测模型：一种自适应多指标进化搜索方法