Differential Detection of Facial Retouching: A Multi-Biometric Approach

Abstract

Facial retouching apps have become common tools which are frequently applied to improve one's facial appearance, e.g. before sharing face images via social media. Beautification induced by retouching has the ability to substantially alter the appearance of face images and hence might represent a challenge for face recognition. Towards deploying secure face recognition as well as enforcing antiphotoshop legislations, a robust and reliable detection of retouched face image is needed. Published approaches consider a single image-based (no-reference) scenario where a potentially retouched face image serves as sole input to the retouching detector. However, in many cases a trusted unaltered face image of a subject examined is available which enables an image pair-based (differential) detection scheme. In this work, ICAO-compliant subsets of the FERET and FRGCv2 face databases are used to automatically create a database containing 9,078 retouched face images together with unconstrained probe images. In evaluations employing the commercial Cognitec FaceVACS and the open-source ArcFace face recognition system, it is shown that facial retouching can negatively impact face recognition performance. Further, a differential facial retouching detection system is proposed which processes pairs of a potentially retouched reference image and corresponding unaltered probe image of single subjects. Estimated differences in feature vectors obtained from texture descriptors, facial landmarks, and deep face representations are leveraged by machine learning-based classifiers of which the detection scores are fused to distinguish between retouched and unaltered face images. The proposed scheme is evaluated in a cross-database scenario where training and testing are performed on the FERET and FRGCv2 databases and vice versa. In the scenario where the used retouching algorithm is known by the detection algorithm, a competitive average D-EER of approximately 2% is achieved. Further, the scenario in which the employed retouching algorithm is not known by the detection algorithm is evaluated. In the latter scenario, the proposed approach obtains an average D-EER below 10% and is shown to outperform several state-of-the-art single image-based detection schemes.

Face Morphing Attack Generation and Detection: A Comprehensive Survey

“…Another aspect that has not yet been investigated with regard to its impact on MAD is potential face beautification. It is expected that face images are beautified prior to applying for a passport in many countries [97]. As the beautification process changes the image properties, it is essential to understand both vulnerability and MAD for this particular problem.…”

Makeup Presentation Attacks: Review and Detection Performance Benchmark

“…Differences which indicate M-PAs can be learned in a training stage employing a machine learningbased classifier. Differential attack detection systems have already been successfully proposed for detection of face morphing [50] and facial retouching [51]. Therefore, main focus is put on differential M-PAD.…”

“…In contrast, differential detection methods aiming to unveil digital image manipulation, e.g. [50], [51], consider the probe image to be a trusted live capture. In rare scenarios, a differential M-PAD might not be possible, i.e.…”

Resampling Estimation based RPC Metadata Verification in Satellite Imagery

“…There is extensive recent work on image forensics, including techniques to detect image splicing based forgeries ( [5], [6]), copy-move forgeries ( [7], [8], [1]), image retouching ( [9], [10]), seam carving ( [11], [12]) and image resampling ( [13], [14]). Other common manipulations include machine learning based forgeries, typically manipulated using Generative Adversarial Networks (GANs) ( [15], [16]).…”