通过文本引导扩散实现隐私保护的可控对抗妆容
Controllable Adversarial Makeup for Privacy via Text-Guided Diffusion
AI总结：
本文提出 MASQUE 扩散框架，利用文本提示、空文本反演、掩码交叉注意力融合和成对对抗引导生成局部对抗妆容，在无需目标身份的情况下提升反人脸识别躲避成功率与视觉保真度。
摘要：
随着人脸识别在政府和商业服务中日益普及，其潜在滥用引发了人们对隐私和公民权利的严重担忧。为应对这一威胁，研究者提出了多种反人脸识别技术，通过对抗性扰动人脸图像来保护隐私。其中，基于生成式妆容的方法研究最为广泛。然而，这些方法主要旨在冒充特定目标身份，只能实现较弱的躲避成功率，同时增加了被定向滥用的风险。此外，它们往往会引入全局视觉伪影，或缺乏适应多样妆容提示的能力，从而影响用户满意度。为解决上述局限，我们开发了 MASQUE，这是一种新颖的基于扩散的框架，可在用户自定义文本提示引导下生成局部对抗妆容。MASQUE 建立在精确的 null-text inversion（空文本反演）、带掩码的定制化交叉注意力融合，以及利用同一个体图像的成对对抗引导机制之上，无需任何外部身份即可实现稳健的躲避性能。在开源人脸识别模型和商业 API 上的综合评估表明，MASQUE 相比所有基线显著提高了躲避成功率，同时具有更高的感知保真度保持能力、对各种妆容提示更强的适应性，以及对图像变换的鲁棒性。
英文摘要：
As face recognition becomes more widespread in government and commercial services, its potential misuse raises serious concerns about privacy and civil rights. To counteract this threat, various anti-facial recognition techniques have been proposed, which protect privacy by adversarially perturbing face images. Among these, generative makeup-based approaches are the most widely studied. However, these methods, designed primarily to impersonate specific target identities, can only achieve weak dodging success rates while increasing the risk of targeted abuse. In addition, they often introduce global visual artifacts or a lack of adaptability to accommodate diverse makeup prompts, compromising user satisfaction. To address the above limitations, we develop MASQUE, a novel diffusion-based framework that generates localized adversarial makeups guided by user-defined text prompts. Built upon precise null-text inversion, customized cross-attention fusion with masking, and a pairwise adversarial guidance mechanism using images of the same individual, MASQUE achieves robust dodging performance without requiring any external identity. Comprehensive evaluations on open-source facial recognition models and commercial APIs demonstrate that MASQUE significantly improves dodging success rates over all baselines, along with higher perceptual fidelity preservation, stronger adaptability to various makeup prompts, and robustness to image transformations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
