Recent research found that attacks based on morphed face images, i.e., morphing attacks, pose a severe security risk to face recognition systems. A reliable morphing attack detection from a single face image remains a research challenge since cameras and morphing techniques used by an attacker are unknown at the time of classification. These issues are commonly overseen while many researchers report encouraging detection performance for training and testing morphing attack detection schemes on images obtained from a single face database employing a single morphing algorithm. In this work, a morphing attack detection system based on the analysis of Photo Response Non-Uniformity (PRNU) is presented. More specifically, spatial and spectral features extracted from PRNU patterns across image cells are analyzed. Differences of these features for bona fide and morphed images are estimated during a threshold-selection stage using the Dresden image database which is specifically built for PRNU analysis in digital image forensics. Cross-database evaluations are then conducted employing an ICAO compliant subset of the FRGCv2 database and a Print-Scan database which is a printed and scanned version of said FRGCv2 subset. Bona fide and morphed face images are automatically generated employing four different morphing algorithms. The proposed PRNU-based morphing attack detector is shown to robustly distinguish bona fide and morphed face images achieving an average D-EER of 11.2% in the best configuration. In scenarios where image sources and morphing techniques are unknown, it is shown to significantly outperform other previously established morphing attack detectors. Finally, the limitations and potential of the approach are demonstrated on a dataset of printed and scanned bona fide and morphed face images.
No abstract
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. Fig. 1 Application of a beautification app: (a) original image, (b) retouched image, and (c) main differences between (a) and (b) (a) Original, (b) Retouched, (c) Differences
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