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.
翻译:自采用GDPR和CCPA立法以来,公共和私人面部图像数据集都日益受到仔细审查。一些数据集已经完全脱线,有些已匿名化。然而,匿名化如何影响检测性能,尚不清楚匿名化如何影响匿名化对检测性能的影响。据我们所知,本文件介绍了关于图像匿名化对监控面部探测器培训的影响的第一次实证研究。我们比较了传统匿名化器与三种最先进的General Adversarial 网络(GAN)系统(GAN)系统(GAN)系统)方法,通过培训一个现成的面部探测器,对匿名化方法是否适合维护面部探测器的性能、对匿名化工艺的过度训练效果、培训匿名化器的数据集大小以及匿名化GAN系统培训时间的影响。最后一项实验调查了通用GAN系统评估性能和经过培训的面部探测器(GAN)的性能。尽管所有测试的匿名化方法都降低了经培训的面部位探测器的性能,但我们用GAN系统进行最精确化的性变现,因此,在常规性变现后,在GAN系统进行最精确化的性变化前的性变化的状态中将使得GAN系统进行最小的性变化的性变化,在常规的性能学上可以进行最小的变化的状态变化的变现。