LDFA: Latent Diffusion Face Anonymization for Self-driving Applications
- URL: http://arxiv.org/abs/2302.08931v1
- Date: Fri, 17 Feb 2023 15:14:00 GMT
- Title: LDFA: Latent Diffusion Face Anonymization for Self-driving Applications
- Authors: Marvin Klemp, Kevin R\"osch, Royden Wagner, Jannik Quehl, Martin Lauer
- Abstract summary: We introduce a novel deep learning-based pipeline for face anonymization in the context of ITS.
We propose a two-stage method, which contains a face detection model followed by a latent diffusion model to generate realistic face in-paintings.
Our experiment reveal that our pipeline is better suited to anonymize data for segmentation than naive methods and performes comparably with recent GAN-based methods.
- Score: 3.501026362812183
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In order to protect vulnerable road users (VRUs), such as pedestrians or
cyclists, it is essential that intelligent transportation systems (ITS)
accurately identify them. Therefore, datasets used to train perception models
of ITS must contain a significant number of vulnerable road users. However,
data protection regulations require that individuals are anonymized in such
datasets. In this work, we introduce a novel deep learning-based pipeline for
face anonymization in the context of ITS. In contrast to related methods, we do
not use generative adversarial networks (GANs) but build upon recent advances
in diffusion models. We propose a two-stage method, which contains a face
detection model followed by a latent diffusion model to generate realistic face
in-paintings. To demonstrate the versatility of anonymized images, we train
segmentation methods on anonymized data and evaluate them on non-anonymized
data. Our experiment reveal that our pipeline is better suited to anonymize
data for segmentation than naive methods and performes comparably with recent
GAN-based methods. Moreover, face detectors achieve higher mAP scores for faces
anonymized by our method compared to naive or recent GAN-based methods.
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