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This work reveals an intrinsic vulnerability in stereo cameras that stems from their pixel sampling and calibration processes, which can influence the outputs of stereo matching algorithms and proposes a novel strategy that leverages similarity scores to dynamically detect and suppress the depth discrepancies.
Targeted illumination attacks can reduce VLA model task success rates to zero, exposing a critical flaw in current defense strategies that misinterpret color information.
Ghost points, often ignored in LiDAR processing, can be reliably detected and removed using full-waveform LiDAR data, leading to dramatic improvements in SLAM and object detection.
Forget signal injection – a strategically placed, actuated mirror can now hijack even the most secure LiDAR SLAM systems, inducing localization errors exceeding 6 meters.
Even in feature-rich environments, LiDAR SLAM systems are vulnerable to a new spoofing attack (D-SLAMSpoof) that injects dynamically coordinated spurious point clouds, but can be defended against using inertial dead reckoning.