[2603.03665] Machine Pareidolia: Protecting Facial Image with Emotional Editing

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.03665 (cs) [Submitted on 4 Mar 2026] Title:Machine Pareidolia: Protecting Facial Image with Emotional Editing Authors:Binh M. Le, Simon S. Woo View a PDF of the paper titled Machine Pareidolia: Protecting Facial Image with Emotional Editing, by Binh M. Le and Simon S. Woo View PDF HTML (experimental) Abstract:The proliferation of facial recognition (FR) systems has raised privacy concerns in the digital realm, as malicious uses of FR models pose a significant threat. Traditional countermeasures, such as makeup style transfer, have suffered from low transferability in black-box settings and limited applicability across various demographic groups, including males and individuals with darker skin tones. To address these challenges, we introduce a novel facial privacy protection method, dubbed \textbf{MAP}, a pioneering approach that employs human emotion modifications to disguise original identities as target identities in facial images. Our method uniquely fine-tunes a score network to learn dual objectives, target identity and human expression, which are jointly optimized through gradient projection to ensure convergence at a shared local optimum. Additionally, we enhance the perceptual quality of protected images by applying local smoothness regularization and optimizing the score matching loss within our network. Empirical experiments demonstrate that our innovative approach surpasses previous baselines...