DEF CON 32 - Securing CCTV Cameras Against Blind Spots - Jacob Shams

In recent years, CCTV footage has been integrated in systems to observe areas and detect traversing malicious actors (e.g., criminals, terrorists). However, this footage has "blind spots", areas where objects are detected with lower confidence due to their angle/distance from the camera. In this talk, we investigate a novel side effect of object detection in CCTV footage; location-based confidence weakness. We demonstrate that a pedestrian's position (distance, angle, height) in footage impacts an object detector's confidence. We analyze this phenomenon in four lighting conditions (lab, morning, afternoon, night) using five object detectors (YOLOv3, Faster R-CNN, SSD, DiffusionDet, RTMDet). We then demonstrate this in footage of pedestrian traffic from three locations (Broadway, Shibuya Crossing, Castro Street), showing they contain "blind spots" where pedestrians are detected with low confidence. This persists across various locations, object detectors, and times of day. A malicious actor could take advantage of this to avoid detection. We propose TipToe, a novel evasion attack leveraging "blind spots" to construct a minimum confidence path between two points in a CCTV-recorded area. We demonstrate its performance on footage of Broadway, Shibuya Crossing, and Castro Street, observed by YOLOv3, Faster R-CNN, SSD, DiffusionDet, and RTMDet. TipToe reduces max/average confidence by 0.10 and 0.16, respectively, on paths in Shibuya Crossing observed by YOLOv3, with similar performance for other locations and object detectors.