MAP-GAN: multi-attribute facial privacy protection model without losing identification

Abstract In recent years, the proliferation of facial image collection systems coupled with significant advancements in machine learning-driven image analysis techniques has posed formidable challenges to protecting individuals’ privacy information, raising concerns about the security of such sensitive data. The current state-of-the-art technology is adept at extracting an array of intimate personal privacy details, encompassing gender, race, and potentially more, from a solitary facial image, underscoring the intricacies and implications of data privacy. Therefore, there is an urgent need for research on models that can protect the privacy of facial images. To tackle this issue, we proposed a multi-attribute privacy-preserving computational model based on generative adversarial networks (MAP-GAN) to protect sensitive facial privacy attribute information at the image level. For MAP-GAN, we meticulously design a privacy preservation loss function and introduce an attribute probability scoring mechanism to address the problem of binary attribute privacy protection flipping observed in previous models. Additionally, we incorporate an $$\textit{L}_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> information loss constraint to ensure the practical superiority of MAP-GAN by maintaining the information gap between privacy-protected images and original images. To enhance the quality of generated privacy-preserving images, we present a privacy-preserving image generator that utilizes residual structures and selective transmission units in MAP-GAN’s design. Experimental results demonstrate that MAP-GAN outperforms other models in terms of both multi-attribute privacy protection and utility.