用微调关键部位+透明层让人脸躲过识别，人眼可见机器却认不出。
Novel AI Camera Camouflage: Face Cloaking Without Full Disguise
- 在眉骨、鼻梁、下颌线等关键点做微小扰动
- 使机器识别率下降，但人眼仍能辨认
- 适合关注隐私的普通用户或反监控场景
本研究提出一种新型人脸伪装方法，结合局部化妆品扰动与PNG图像的透明度层攻击，有效规避现代人脸识别系统。不同于以往的视觉干扰、对抗补丁或戏剧化伪装，该方法通过在密集关键点区域施加垂直扰动，显著破坏检测效果。实验使用Haar级联分类器及BetaFaceAPI、Microsoft Bing视觉搜索等商用系统验证，结果显示在关键点附近进行细微调整即可实现高效遮蔽。同时，利用PNG图像中的透明度层，实现双层效果：人类观察时人脸清晰可见，但在机器可读的RGB通道中消失，导致无法被反向图像搜索识别。该方法展示了低可见性、可扩展的面部模糊策略，在保障隐私的同时维持合理匿名性。
原文摘要 · Abstract (English)
This study demonstrates a novel approach to facial camouflage that combines targeted cosmetic perturbations and alpha transparency layer manipulation to evade modern facial recognition systems. Unlike previous methods -- such as CV dazzle, adversarial patches, and theatrical disguises -- this work achieves effective obfuscation through subtle modifications to key-point regions, particularly the brow, nose bridge, and jawline. Empirical testing with Haar cascade classifiers and commercial systems like BetaFaceAPI and Microsoft Bing Visual Search reveals that vertical perturbations near dense facial key points significantly disrupt detection without relying on overt disguises. Additionally, leveraging alpha transparency attacks in PNG images creates a dual-layer effect: faces remain visible to human observers but disappear in machine-readable RGB layers, rendering them unidentifiable during reverse image searches. The results highlight the potential for creating scalable, low-visibility facial obfuscation strategies that balance effectiveness and subtlety, opening pathways for defeating surveillance while maintaining plausible anonymity.
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