Over the past years, deep learning capabilities and the availability of large-scale training
datasets advanced rapidly, leading to breakthroughs in face recognition accuracy. However …

Face recognition has been successfully deployed in real-time applications, including secure
applications such as border control. The vulnerability of face recognition systems (FRSs) to …

Over the past two decades, biometric recognition has exploded into a plethora of different
applications around the globe. This proliferation can be attributed to the high levels of …

The main question this work aims at answering is:" can morphing attack detection (MAD)
solutions be successfully developed based on synthetic data?". Towards that, this work …

Deep neural networks have become prevalent in human analysis, boosting the performance
of applications, such as biometric recognition, action recognition, as well as person re …

Morphing attacks are a threat to biometric systems where the biometric reference in an
identity document can be altered. This form of attack presents an important issue in …

Deepfakes present an emerging threat in cyberspace. Recent developments in machine
learning make deepfakes highly believable, and very difficult to differentiate between what is …

Generative deep learning techniques have invaded the public discourse recently. Despite
the advantages, the applications to disinformation are concerning as the counter-measures …

A face morphing attack image can be verified to multiple identities, making this attack a
major vulnerability to processes based on identity verification, such as border checks …

This paper presents a summary of the Competition on Face Morphing Attack Detection
Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 In-ternational …