http://dl.gi.de/handle/20.500.12116/34315 2026-07-21T13:43:48Z 2026-07-21T13:43:48Z Nielsen, V Khodabakhsh, Ali Busch, Christoph http://dl.gi.de/handle/20.500.12116/34348 2020-09-16T08:25:50Z 2020-01-01T00:00:00Z

Unit-Selection Based Facial Video Manipulation Detection Nielsen, V; Khodabakhsh, Ali; Busch, Christoph Brömme, Arslan; Busch, Christoph; Dantcheva, Antitza; Raja, Kiran; Rathgeb, Christian; Uhl, Andreas Advancements in video synthesis technology have caused major concerns over the authenticity of audio-visual content. A video manipulation method that is often overlooked is inter-frame forgery, in which segments (or units) of an original video are reordered and rejoined while cut-points are covered with transition effects. Subjective tests have shown the susceptibility of viewers in mistaking such content as authentic. In order to support research on the detection of such manipulations, we introduce a large-scale dataset of 1000 morph-cut videos that were generated by automation of the popular video editing software Adobe Premiere Pro. Furthermore, we propose a novel differential detection pipeline and achieve an outstanding frame-level detection accuracy of 95%.

2020-01-01T00:00:00Z Medvedev, V Gonçalves, Nuno Cruz, Leandro http://dl.gi.de/handle/20.500.12116/34346 2020-09-16T08:25:50Z 2020-01-01T00:00:00Z

Biometric System for Mobile Validation of ID And Travel Documents Medvedev, V; Gonçalves, Nuno; Cruz, Leandro Brömme, Arslan; Busch, Christoph; Dantcheva, Antitza; Raja, Kiran; Rathgeb, Christian; Uhl, Andreas Current trends in security of ID and travel documents require portable and efficient validation applications that rely on biometric recognition. Such tools can allow any authority and citizen to validate documents and authenticate citizens with no need of expensive and sometimes unavailable proprietary devices. In this work, we present a novel, compact and efficient approach of validating ID and travel documents for offline mobile applications. The approach employs the in-house biometric template that is extracted from the original portrait photo (either full frontal or token frontal), and then stored on the ID document with use of a machine readable code (MRC). The ID document can then be validated with a developed application on a mobile device with digital camera. The similarity score is estimated with use of an artificial neural network (ANN). Results show that we achieve validation accuracy up to 99.5% with corresponding false match rate = 0.0047 and false non-match rate = 0.00034.

2020-01-01T00:00:00Z Mitkovski, Aleksandar Merkle, Johannes Rathgeb, Christian Tams, Benjamin Bernardo, Kevin Haryanto, Nathania E. Busch, Christoph http://dl.gi.de/handle/20.500.12116/34347 2020-09-16T08:25:50Z 2020-01-01T00:00:00Z

Simulation of Print-Scan Transformations for Face Images based on Conditional Adversarial Networks Mitkovski, Aleksandar; Merkle, Johannes; Rathgeb, Christian; Tams, Benjamin; Bernardo, Kevin; Haryanto, Nathania E.; Busch, Christoph Brömme, Arslan; Busch, Christoph; Dantcheva, Antitza; Raja, Kiran; Rathgeb, Christian; Uhl, Andreas In many countries, printing and scanning of face images is frequently performed as part of the issuance process of electronic travel documents, e.g., ePassports. Image alterations induced by such print-scan transformations may negatively effect the performance of various biometric subsystems, in particular image manipulation detection. Consequently, according training data is needed in order to achieve robustness towards said transformations. However, manual printing and scanning is time-consuming and costly. In this work, we propose a simulation of print-scan transformations for face images based on a Conditional Generative Adversarial Network (cGAN). To this end, subsets of two public face databases are manually printed and scanned using different printer-scanner combinations. A cGAN is then trained to perform an image-to-image translation which simulates the corresponding print-scan transformations. The goodness of simulation is evaluated with respect to image quality, biometric sample quality and performance, as well as human assessment.

2020-01-01T00:00:00Z González-Soler, Lázaro J. Gomez-Barrero, Marta Busch, Christoph http://dl.gi.de/handle/20.500.12116/34343 2020-09-16T08:25:49Z 2020-01-01T00:00:00Z

Fisher Vector Encoding of Dense-BSIF Features for Unknown Face Presentation Attack Detection González-Soler, Lázaro J.; Gomez-Barrero, Marta; Busch, Christoph Brömme, Arslan; Busch, Christoph; Dantcheva, Antitza; Raja, Kiran; Rathgeb, Christian; Uhl, Andreas The task of determining whether a sample stems from a real subject (i.e, it is a bona fide presentation) or it comes from an artificial replica (i.e., it is an attack presentation) is a mandatory requirement for biometric capture devices, which has received a lot of attention in the recent past. Nowadays, most face Presentation Attack Detection (PAD) approaches have reported a good detection performance when they are evaluated on known Presentation Attack Instruments (PAIs) and acquisition conditions, in contrast to more challenging scenarios where unknown attacks are included in the evaluation. For those more realistic scenarios, the existing approaches are in many cases unable to detect unknown PAI species. In this work, we introduce a new feature space based on Fisher vectors, computed from compact Binarised Statistical Image Features (BSIF) histograms, which allows finding semantic feature subsets from known samples in order to enhance the detection of unknown attacks. This new representation, evaluated over three freely available facial databases, shows promising results in the top state-of-the-art: a BPCER100 under 17% together with a AUC over 98% can be achieved in the presence of unknown attacks.

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