CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search. (arXiv:2306.10008v1 [cs.CV])

The success of deep learning based face recognition systems has given rise to
serious privacy concerns due to their ability to enable unauthorized tracking
of users in the digital world. Existing methods for enhancing privacy fail to
generate naturalistic images that can protect facial privacy without
compromising user experience. We propose a novel two-step approach for facial
privacy protection that relies on finding adversarial latent codes in the
low-dimensional manifold of a pretrained generative model. The first step
inverts the …

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