Visible Yet Unrecognizable: Frequency-Selective Facial Privacy via Attention

Facial privacy has become a critical concern as unauthorized large-scale image scraping enables malicious actors to collect personal face images without consent and exploit them for training unauthorized recognition systems. Protecting facial privacy requires modifying a face image such that automated recognition systems fail to identify the individual, while the visual appearance remains unchanged to human observers, ensuring the image retains its social utility. However, existing methods fail to satisfy both requirements; they either irreversibly degrade visual quality, rendering images impractical, or alter facial identity to such an extent that the protected image bears no resemblance to the original subject. To address this fundamental privacy-utility tradeoff, we propose MIRAGE (Multi-scale Identity Removal via Attention-Guided Encoding), a frequency-selective facial privacy framework grounded in the observation that identity-discriminative features reside predominantly in low-frequency image components, while visual appearance is encoded in high-frequency components. We further introduce FAC (FrequencyAware Consistency) Loss, which jointly enforces identity separation and visual fidelity during training. A comprehensive evaluation across three benchmark datasets and nine state-of-the-art (SOTA) deep face recognition (DFR) models demonstrates that MIRAGE achieves robust facial privacy protection while preserving visual appearance, successfully bridging the privacy-utility gap left unresolved by existing methods. The code is available at: https://github.com/atulkr05/MIRAGE.git.