Privacy Preservation in Event-Based Vision: Risks, Methods, and Trade-offs

Abstract

Facial recognition systems pose significant privacy risks, encouraging the development of generative adversarial evasion methods, such as AMT-GAN and Adv-CPG. While effective on clean, high-resolution RGB images, it remains unknown whether facial protection methods are still effective under the reconstruction pipeline of event-based cameras. This research investigates the privacy-naturalness trade-off of applying adversarial makeup to event-reconstructed faces. CelebV-HQ video clips were converted to event streams, reconstructed into grayscale images using E2VID under different thresholds, and evaluated for Attack Success Rate (ASR) and Structural Similarity (SSIM). The results reveal the following: the event-reconstruction process reduced AMT-GAN’s protection effectiveness, dropping mean ASR across four white-box models. A contrast-threshold ablation indicated this reduction is a direct result of the event-generation process itself, rather than just data loss from sparse event streams. Furthermore, a qualitative evaluation of Adv-CPG showed serious identity over-shifting and mode collapse, failing to maintain the structural diversity of the reconstructed face-image inputs. Finally, this research shows that current RGB-based adversarial protections are highly sensitive to domain shifts and fail to provide appropriate privacy for event-reconstructed vision. The scripts and jobs used in this paper can be found in the public repository: https://github.com/MateiOpr/research-project