Effect of Privacy Preservation Strategies on Event-to-Image Reconstruction
A Comparative Study of Raw-Event Perturbation Strategies
A. Tamgaç (TU Delft - Electrical Engineering, Mathematics and Computer Science)
T. Parlayici – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
N. Tömen – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
R. Guerra Marroquim – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Event cameras are increasingly deployed in privacy-sensitive applications such as surveillance, autonomous vehicles, and human-computer interaction. Unlike conventional cameras, they record only per-pixel brightness changes as a sparse, asynchronous stream of events, making them efficient and potentially privacy-preserving. However, reconstruction models such as E2VID can recover recognizable facial images from event streams, undermining this assumption. This paper investigates whether simple perturbations applied directly to raw event streams can reduce face identifiability while preserving reconstruction quality. Three perturbation methods are compared: polarity flipping, spatial jitter, and event insertion and deletion. Each method is evaluated across multiple strength levels on 300 face video clips from the CelebV-HQ dataset, converted to synthetic events using v2e. Reconstruction quality is measured using PSNR, SSIM, and LPIPS, while face identifiability is measured using FaceNet re-identification. The implementation is available at GitHub repository.
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