利用人脸美感信息检测数字美颜，提升识别安全。
Detection of Digital Facial Retouching utilizing Face Beauty Information
- 基于美感评估算法分析美颜图像变化
- 未知美颜算法下单图检测达1.1% D-EER
- 适合生物识别安全与图像真实性验证
美颜修饰在社交媒体、广告及专业摄影中广泛应用，旨在提升人脸美观度。然而，当经修饰的图像被用作生物特征样本录入系统时，会带来安全隐患。已有研究证实美颜会影响人脸识别性能，因此检测美颜操作变得愈发重要。本文研究并分析了美颜图像对人脸美感评估算法的影响，评估多种人工智能特征提取方法以提升检测效果，并探索利用人脸美感信息增强检测率。在攻击美颜算法未知的场景下，本方法在单图检测任务中实现了1.1%的D-EER（Detection Error Rate），显著提升了检测精度。
原文摘要 · Abstract (English)
Facial retouching to beautify images is widely spread in social media, advertisements, and it is even applied in professional photo studios to let individuals appear younger, remove wrinkles and skin impurities. Generally speaking, this is done to enhance beauty. This is not a problem itself, but when retouched images are used as biometric samples and enrolled in a biometric system, it is one. Since previous work has proven facial retouching to be a challenge for face recognition systems,the detection of facial retouching becomes increasingly necessary. This work proposes to study and analyze changes in beauty assessment algorithms of retouched images, assesses different feature extraction methods based on artificial intelligence in order to improve retouching detection, and evaluates whether face beauty can be exploited to enhance the detection rate. In a scenario where the attacking retouching algorithm is unknown, this work achieved 1.1% D-EER on single image detection.
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