Search Results for author:
Found 7 papers, 4 papers with code
Are GAN-based Morphs Threatening Face Recognition?
Morphing attacks are a threat to biometric systems where the biometric reference in an identity document can be altered.
Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks
Morphing attacks is a threat to biometric systems where the biometric reference in an identity document can be altered.
Deepfake detection: humans vs. machines
In response to the threat such manipulations can pose to our trust in video evidence, several large datasets of deepfake videos and many methods to detect them were proposed recently.
The Speed Submission to DIHARD II: Contributions & Lessons Learned
This paper describes the speaker diarization systems developed for the Second DIHARD Speech Diarization Challenge (DIHARD II) by the Speed team.
pyannote.audio: neural building blocks for speaker diarization
We introduce pyannote. audio, an open-source toolkit written in Python for speaker diarization.
Ranked #1 on
Speaker Diarization
on ETAPE
Vulnerability of Face Recognition to Deep Morphing
We show that the state of the art face recognition systems based on VGG and Facenet neural networks are vulnerable to the deep morph videos, with 85. 62 and 95. 00 false acceptance rates, respectively, which means methods for detecting these videos are necessary.
DeepFakes: a New Threat to Face Recognition? Assessment and Detection
The best performing method, which is based on visual quality metrics and is often used in presentation attack detection domain, resulted in 8. 97% equal error rate on high quality Deepfakes.
