Nowadays, many facial images are acquired using smart phones. To ensure the best outcome, users frequently retouch these images before sharing them, e.g. via social media. Modifications resulting from used retouching algorithms might be a challenge for face recognition technologies. Towards deploying robust face recognition as well as enforcing anti-photoshop legislations, a reliable detection of retouched face images is needed. In this work, the effects of facial retouching on face recognition are investigated. A qualitative assessment of 32 beautification apps is conducted. Based on this assessment five apps are chosen which are used to create a database of 800 beautified face images. Biometric performance is measured before and after retouching using a commercial face recognition system. Subsequently, a retouching detection system based on the analysis of photo response non-uniformity (PRNU) is presented. Specifically, scores obtained from analysing spatial and spectral features extracted from PRNU patterns across image cells are fused. In a scenario, in which unaltered bona fide images are compressed to the average sizes of the retouched images using JPEG, the proposed PRNU-based detection scheme is shown to robustly distinguish between bona fide and retouched images achieving an average detection equal error rate of 13.7% across all retouching algorithms.

中文翻译：

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基于PRNU的面部修饰检测

如今，许多面部图像是使用智能手机获取的。为了确保最佳效果，用户在共享图像之前（例如通过社交媒体）经常润饰这些图像。对于人脸识别技术而言，由使用过的修饰算法导致的修改可能是一个挑战。为了部署可靠的人脸识别以及执行反Photoshop法规，需要可靠地检测修饰后的人脸图像。在这项工作中，研究了面部修饰对面部识别的影响。对32个美化应用程序进行了定性评估。基于此评估，选择了五个应用程序，这些应用程序用于创建包含800张美化面部图像的数据库。使用商用人脸识别系统在修饰前后测量生物识别性能。后来，提出了一种基于光响应不均匀性分析的修饰检测系统。具体而言，将通过分析从跨图像单元的PRNU模式提取的空间和光谱特征获得的分数进行融合。在使用JPEG将未更改的善意图像压缩为修饰图像的平均大小的情况下，建议的基于PRNU的检测方案显示出能可靠地区分善意图像和修饰图像，实现平均检测等错误率为13.7所有修饰算法的百分比。