  Since the introduction of the GDPR and CCPA legislation, both public and
private facial image datasets are increasingly scrutinized. Several datasets
have been taken offline completely and some have been anonymized. However, it
is unclear how anonymization impacts face detection performance. To our
knowledge, this paper presents the first empirical study on the effect of image
anonymization on supervised training of face detectors. We compare conventional
face anonymizers with three state-of-the-art Generative Adversarial
Network-based (GAN) methods, by training an off-the-shelf face detector on
anonymized data. Our experiments investigate the suitability of anonymization
methods for maintaining face detector performance, the effect of detectors
overtraining on anonymization artefacts, dataset size for training an
anonymizer, and the effect of training time of anonymization GANs. A final
experiment investigates the correlation between common GAN evaluation metrics
and the performance of a trained face detector. Although all tested
anonymization methods lower the performance of trained face detectors, faces
anonymized using GANs cause far smaller performance degradation than
conventional methods. As the most important finding, the best-performing GAN,
DeepPrivacy, removes identifiable faces for a face detector trained on
anonymized data, resulting in a modest decrease from 91.0 to 88.3 mAP. In the
last few years, there have been rapid improvements in realism of GAN-generated
faces. We expect that further progression in GAN research will allow the use of
Deep Fake technology for privacy-preserving Safe Fakes, without any performance
degradation for training face detectors.
