Frequency-domain Manipulation for Face Obfuscation

Facial image datasets have become essential resources for various face analysis tasks, but their use raises significant privacy concerns. To address this issue, face obfuscation has emerged as a practical approach to hide identity from humans while retaining cues decipherable by machines. However, existing methods often leave exploitable visual traces, making them vulnerable to reconstruction attacks that restore hidden identity. To address this issue, we propose a frequency-domain manipulation framework, called FreM, which adjusts frequency subbands differently to hide identity, retain machine-decipherable cues, and improve robustness against reconstruction attacks. Specifically, the proposed FreM first decomposes a facial image into frequency subbands and applies subband-adaptive modulation that regulates information according to the characteristics of each subband. The modulation parameters are then refined to yield the reliable obfuscated result. Extensive experiments across multiple face analysis benchmarks demonstrate that FreM achieves superior obfuscation quality and strong robustness against reconstruction attacks. The source code will be made publicly available.