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, including noise-based, makeup-based, and freeform attribute methods, in both qualitative fidelity and quantitative metrics. Furthermore, MAP proves its effectiveness against an online FR API and shows advanced adaptability in uncommon photographic scenarios.
Abstract (translated)
面部识别（FR）系统的普及在数字领域引发了隐私问题，因为恶意使用这些系统对个人安全构成了重大威胁。传统的对策，如化妆风格转移，在黑盒设置中传输效果不佳，并且在不同的人群，包括男性和肤色较深的个体之间适用性有限。为了解决这些问题，我们提出了一种新的面部隐私保护方法——称为**MAP**的方法，它采用人类情感修改技术来将原始身份伪装成目标身份。我们的方法独特地调整了一个评分网络以同时学习两个目标：目标身份和人类表情，并通过梯度投影联合优化这两个目标，确保在共享局部最优中收敛。 此外，我们通过应用局部平滑正则化并优化评分匹配损失来提高保护图像的感知质量。实验证明，我们的创新方法在定性和定量指标上都超越了以前的基础线方法，包括基于噪声、化妆和自由形式属性的方法。此外，MAP证明了其对抗在线面部识别API的有效性，并展示了在不常见摄影场景中的先进适应能力。
URL
https://arxiv.org/abs/2603.03665
