Abstract
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.
Abstract (translated)
面部修图在社交媒体、广告中非常普遍，甚至专业摄影棚也使用这种方法让个人看起来更年轻、去除皱纹和皮肤瑕疵。通常来说，这种做法是为了提升美感，并无大碍；然而，当经过修饰的图像被用作生物识别样本并录入到生物识别系统时，问题就出现了。之前的研究已经证明面部修图对人脸识别系统构成了挑战，因此检测面部修图变得越来越必要。 本研究旨在探讨和分析修图后图像在美容评估算法中的变化，评估基于人工智能的不同特征提取方法以提高修图检测的准确性，并检验脸部美感是否可以被利用来提升检测率。在攻击性修图算法未知的情境下，该研究仅凭单张图片检测就能达到1.1%的虚警误报率（D-EER）。
URL
https://arxiv.org/abs/2512.08397
