Swapping Faces, Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Roba H. Farouk, Catherine M. Elias
• Venue: arXiv: cs.CV
• Published: 2026-07-09
• Collected: 2026-07-10
• URL: http://arxiv.org/abs/2607.08402v1
• PDF: https://arxiv.org/pdf/2607.08402v1
• DOI: UNKNOWN

Plain-Language Brief

This research focuses on creating a five-stage pipeline to protect pedestrians' privacy in autonomous vehicles (AVs) by using a face-swapping method called Roop, which outperforms Ghost-v2 and strikes a balance between privacy and data usability.

What the Document Says

• The work aims to implement a five-stage pipeline for preserving pedestrian privacy in autonomous vehicles (AVs).
• The method involves face swapping to conceal identities while maintaining essential facial attributes.
• The pipeline is designed to cater to the privacy needs of the Egy-DRiVeS dataset.
• Two face-swapping models, Roop and Ghost-v2, are evaluated in this work.
• Roop outperforms Ghost-v2 in various aspects according to the text.

Key Concepts Mentioned

• autonomous vehicles
• pedestrian privacy
• face swapping
• Egy-DRiVeS dataset
• Roop
• Ghost-v2

Claims Made by the Document

• The proposed pipeline preserves pedestrian privacy while maintaining image usability.
• Roop outperforms Ghost-v2 in various aspects.

Evidence Used

• Implementation of a five-stage pipeline for pedestrian privacy in AVs
• Evaluation of Roop and Ghost-v2 face-swapping models

Methods / Approach

• Face swapping using the Roop model

Limitations Stated by the Authors

Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

The paper does not discuss potential limitations or challenges other than those mentioned by the authors.

Open Questions

• What specific aspects does Roop outperform Ghost-v2 in?
• What are the details of the five-stage pipeline?

Notes for Future Q&A

May include interpretation. Labeled where it does.

• The text mentions that Roop outperforms Ghost-v2 in various aspects, but it doesn't specify what these aspects are.

Raw Abstract / Excerpt

Large-scale and diverse datasets are needed to train AI models to take real-time decisions for autonomous vehicles (AVs), an intelligent transportation system (ITS) application. Pedestrian intention and trajectory prediction are critical models used in AVs, requiring datasets involving diverse pedestrian images. Unrestricted access to these datasets imposes serious security risks, like identity theft and pedestrian tracking. The challenge is to apply privacy preservation procedures while maintaining the image attributes needed to train the models. Existing privacy methods may preserve the pedestrian's privacy, but degrade the image usability, which hinders the models' effectiveness. This work's focus is to implement a five-stage pipeline to protect pedestrians' privacy through face swapping while keeping the essential facial attributes intact. It should be tailored to satisfy the privacy needs of the Egy-DRiVeS dataset. Moreover, Roop and Ghost-v2 face-swapping models are evaluated. Provenly, Roop outperforms Ghost-v2 in various aspects, as will be discussed. Consequently, Roop is the face-swapping model to be used in the pipeline to strike the balance between pedestrian privacy via identity concealment and data usability via facial attribute preservation.