论文标题
对抗检测：实时攻击对象检测
Adversarial Detection: Attacking Object Detection in Real Time
论文作者
论文摘要
智能机器人依靠对象检测模型来感知环境。在深度学习安全方面的进步之后，已经揭示了对象检测模型容易受到对抗性攻击的影响。但是，先前的研究主要集中于攻击静态图像或离线视频。因此，尚不清楚这种攻击是否会危害动态环境中的现实世界机器人应用。本文通过提出针对对象检测模型的第一次实时在线攻击来弥合这一差距。我们设计了三个攻击，这些攻击在所需位置为不存在的对象制造边界框。攻击在大约20次迭代中获得了约90％的成功率。该演示视频可从https://youtu.be/zjz1anlxsmu获得。
Intelligent robots rely on object detection models to perceive the environment. Following advances in deep learning security it has been revealed that object detection models are vulnerable to adversarial attacks. However, prior research primarily focuses on attacking static images or offline videos. Therefore, it is still unclear if such attacks could jeopardize real-world robotic applications in dynamic environments. This paper bridges this gap by presenting the first real-time online attack against object detection models. We devise three attacks that fabricate bounding boxes for nonexistent objects at desired locations. The attacks achieve a success rate of about 90% within about 20 iterations. The demo video is available at https://youtu.be/zJZ1aNlXsMU.
