Controllable Face Privacy

30 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

I-Pic: A Platform for Privacy-Compliant Image Capture (2016, background): “For instance, it is possible to morph a face into another face [10] instead of blurring it.”
FlowSAN: Privacy-Enhancing Semi-Adversarial Networks to Confound Arbitrary Face-Based Gender Classifiers (2019, background): “RELATED WORK With regard to privacy concerns in recent years, a new line of research has emerged that focuses on methods for imparting soft-biometric privacy to biometric data and face images in particular [8]–[10], [23], [24], [46].”
Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images (2017, methodology): “On the other hand, as inspired by the work of Othman and Ross [21] and later promoted by Sim and Zhang [27], the goal is to selectively confound or preserve a set of attributes that can be deduced from face images.”