一种无需使用对抗性配件即可规避街道视频摄像头检测的隐私增强技术
A Privacy Enhancing Technique to Evade Detection by Street Video Cameras Without Using Adversarial Accessories
AI总结：
本文提出一种利用行人检测器位置弱点的隐私增强技术，通过构建最小置信度路径帮助行人规避检测，并提出相应反制措施以提高盲区检测置信度。
摘要：
在本文中，我们提出了一种隐私增强技术，该技术利用了自动行人检测算法的固有特性，即基于深度神经网络（DNN）的方法通常使用精选的数据集和实验室环境进行训练，而这些方法的实际运行区域是动态的真实世界环境。具体而言，我们利用了这种实验室与真实世界之间的差距所产生的一种新副作用：行人检测中基于位置的弱点。我们证明，一个人的位置（距离、角度、高度）以及环境光照水平会直接影响行人检测器在检测该人时的置信度。随后，我们证明了这种现象存在于对静止的行人交通场景进行检测的行人检测器中，其中存在由于对行人的检测置信度较低而产生的弱检测盲区。我们展示了注重隐私的行人如何利用这些盲区来规避检测，方法是通过在场景中的两点之间构建一条最小置信度路径，与直接穿过场景和随机穿过场景的路径相比，该路径的最大置信度和平均置信度分别最多降低了0.09和0.04。为了应对这一现象，迫使攻击者不得不使用成本更高、更复杂的方法来利用这一漏洞，我们提出了一种新的反制措施，以提高行人检测器在盲区中的检测置信度，从而使我们的技术生成的路径的最大置信度和平均置信度分别提高了0.09和0.05。在反制措施中，我们证明，我们的反制方法使基于faster R-CNN的行人检测器的真正例平均真阳性置信度分别提高了0.03和0.15。
英文摘要：
In this paper, we propose a privacy-enhancing technique leveraging an inherent property of automatic pedestrian detection algorithms, namely, that the training of deep neural network (DNN) based methods is generally performed using curated datasets and laboratory settings, while the operational areas of these methods are dynamic real-world environments. In particular, we leverage a novel side effect of this gap between the laboratory and the real world: location-based weakness in pedestrian detection. We demonstrate that the position (distance, angle, height) of a person, and ambient light level, directly impact the confidence of a pedestrian detector when detecting the person. We then demonstrate that this phenomenon is present in pedestrian detectors observing a stationary scene of pedestrian traffic, with blind spot areas of weak detection of pedestrians with low confidence. We show how privacy-concerned pedestrians can leverage these blind spots to evade detection by constructing a minimum confidence path between two points in a scene, reducing the maximum confidence and average confidence of the path by up to 0.09 and 0.13, respectively, over direct and random paths through the scene. To counter this phenomenon, and force the use of more costly and sophisticated methods to leverage this vulnerability, we propose a novel countermeasure to improve the confidence of pedestrian detectors in blind spots, raising the max/average confidence of paths generated by our technique by 0.09 and 0.05, respectively. In addition, we demonstrate that our countermeasure improves a Faster R-CNN-based pedestrian detector's TPR and average true positive confidence by 0.03 and 0.15, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
