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M. Mocanu
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Event-based cameras are often considered more privacy preserving than conventional RGB cameras because they don’t capture full image frames, colour, or texture. Nonetheless, their raw event streams might still encode structural information about the recorded scene. This paper questions this assumption and investigates this privacy concern experimentally by converting raw events into direct event representations and evaluating whether machine-learning models can recover semantic, spatial, and motion structure without explicit image reconstruction. Three forms of leakage are studied: semantic leakage through segmentation, spatial layout leakage through depth estimation, and motion leakage through optical flow estimation. All experiments are mainly based on DSEC dataset. As an extension dataset, PEDRo is used to investigate human-specific semantic leakage. The segmentation experiments show that semantic leakage is present but uneven: large and persistent driving scene regions such as road and background are recovered more reliably than sparse human regions in DSEC, while PEDRo shows clearer leakage of approximate human location through human-box segmentation. The depth estimation experiment shows that event representations preserve enough geometric information for a pretrained model to recover coarse scene depth. The optical flow experiment further outlines that event streams preserve recoverable motion information, since a pretrained event-based model can estimate dense motion patterns from the data. These findings highlight that event cameras don’t guarantee privacy by sensor design alone and that privacy in event-based vision depends on the representation, temporal window, task, model, and dataset. The segmentation code used in this project is available at https://github.com/ilincamaria03/event_camera_segmentation.
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Event-based cameras are often considered more privacy preserving than conventional RGB cameras because they don’t capture full image frames, colour, or texture. Nonetheless, their raw event streams might still encode structural information about the recorded scene. This paper questions this assumption and investigates this privacy concern experimentally by converting raw events into direct event representations and evaluating whether machine-learning models can recover semantic, spatial, and motion structure without explicit image reconstruction. Three forms of leakage are studied: semantic leakage through segmentation, spatial layout leakage through depth estimation, and motion leakage through optical flow estimation. All experiments are mainly based on DSEC dataset. As an extension dataset, PEDRo is used to investigate human-specific semantic leakage. The segmentation experiments show that semantic leakage is present but uneven: large and persistent driving scene regions such as road and background are recovered more reliably than sparse human regions in DSEC, while PEDRo shows clearer leakage of approximate human location through human-box segmentation. The depth estimation experiment shows that event representations preserve enough geometric information for a pretrained model to recover coarse scene depth. The optical flow experiment further outlines that event streams preserve recoverable motion information, since a pretrained event-based model can estimate dense motion patterns from the data. These findings highlight that event cameras don’t guarantee privacy by sensor design alone and that privacy in event-based vision depends on the representation, temporal window, task, model, and dataset. The segmentation code used in this project is available at https://github.com/ilincamaria03/event_camera_segmentation.