面向人脸混淆的频域操控
Frequency-domain Manipulation for Face Obfuscation
把频域子带自适应调制当作一种可复用的隐私保护手段：按子带特性分别调节信息量，在隐藏身份的同时保留机器可解读线索，并抵御重建攻击；做视觉数据集发布或隐私敏感场景的人脸分析时可直接借鉴这一频域处理思路。
原文依据 we propose a frequency-domain manipulation framework, called FreM, which adjusts frequency subbands differently to hide identity
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论人脸图像数据集已成为各类人脸分析任务的重要资源，但其使用也引发了显著的隐私担忧。为解决这一问题，人脸混淆作为一种实用方法被提出，其目标是在向人类隐藏身份的同时保留机器可解读的线索。然而，现有方法往往会留下可被利用的视觉痕迹，使其容易受到能够恢复隐藏身份的重建攻击。为解决该问题，我们提出了一个频域操控框架，称为 FreM，它对不同频率子带进行差异化调整，以隐藏身份、保留机器可解读的线索，并提升对重建攻击的鲁棒性。具体而言，所提出的 FreM 首先将人脸图像分解为频率子带，并施加子带自适应调制，依据各子带的特性来调控信息。随后对调制参数进行细化，以得到可靠的混淆结果。在多个 face analysis 基准上的大量实验表明，FreM 实现了更优的混淆质量，并对重建攻击具有强鲁棒性。
英文原文摘要 1141 字符
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.