Assessing the Use of Face Swapping Methods as Face Anonymizers in Videos

TL;DR

This paper evaluates face swapping techniques as a means of anonymizing faces in videos, demonstrating their effectiveness in preserving privacy while maintaining visual quality and temporal consistency.

Contribution

It provides a comprehensive assessment of face swapping methods for video anonymization, highlighting their potential and identifying key challenges for future research.

Findings

Face swapping achieves consistent facial transitions in videos.

It effectively conceals personal identities.

The methods maintain visual fidelity and temporal coherence.

Abstract

The increasing demand for large-scale visual data, coupled with strict privacy regulations, has driven research into anonymization methods that hide personal identities without seriously degrading data quality. In this paper, we explore the potential of face swapping methods to preserve privacy in video data. Through extensive evaluations focusing on temporal consistency, anonymity strength, and visual fidelity, we find that face swapping techniques can produce consistent facial transitions and effectively hide identities. These results underscore the suitability of face swapping for privacy-preserving video applications and lay the groundwork for future advancements in anonymization focused face-swapping models.