A Privacy Enhancing Technique to Evade Detection by Street Video Cameras Without Using Adversarial Accessories

• URL: http://arxiv.org/abs/2501.15653v1
• Date: Sun, 26 Jan 2025 19:29:49 GMT
• Title: A Privacy Enhancing Technique to Evade Detection by Street Video Cameras Without Using Adversarial Accessories
• Authors: Jacob Shams, Ben Nassi, Satoru Koda, Asaf Shabtai, Yuval Elovici,
• Abstract summary: We leverage a novel side effect of this gap between the laboratory and the real world: location-based weakness in pedestrian detection. We show how privacy-concerned pedestrians can leverage blind spots to evade detection by constructing a minimum confidence path between two points in a scene. 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.
• Score: 28.431929351359734
• License:
• Abstract: 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.

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