Adversarial Face Privacy Protection Based on Makeup Style Patch Activation

Facial recognition technology has developed rapidly,greatly facilitating people's lives,but it has also raised public concerns about personal privacy.Facial images shared by people through social media and the Internet may be collected by illegal organizations,which can use facial recognition systems to identify the identityand steal privacy information related to the users.Therefore,a privacy protection mechanism is needed to ensure that facial images published by users through public media can be viewed normally by people,but can prevent facial recognition systems from extracting accurate identity information.The mainstream adversarial sample-based methods can solve this problem to some extents,but they inevitably introduce noise that can be easily detected in the images.When people share personal photos on social media and other platforms,they often add some beauty effects.Therefore,embedding adversarial perturbations cleverly while adding beautification effects to the images to achieve identity privacy protection for the images is a win-win choice.In this regard,this paper proposes a facial image identity privacy protection method based on makeup style patch activation.This method activates the makeup style of the reference facial image into the features of the original facial image through feature patches,and then reconstructs the activated features into adversarial facial images with makeup.At the same time,it uses an identity privacy enhancement module to force the generated image's identity features to approach a target identity,thereby obtaining adversarial privacy protection capabilities.Experimental results show that the facial images generated by this method not only have better visual effects and a variety of makeup styles,but also can effectively defend against privacy infringement caused by various black-box facial recognition models.