activity
20182023
most citedDeepFakes: a New Threat to Face Recognition? Assessment and Detection

497 citations · 582 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV2023

Vulnerability of Automatic Identity Recognition to Audio-Visual Deepfakes

Pavel Korshunov, Haolin Chen, Philip N. Garner +1

The task of deepfakes detection is far from being solved by speech or vision researchers. Several publicly available databases of fake synthetic video and speech were built to aid…

cs.CV2022★ 53 cited

Are GAN-based Morphs Threatening Face Recognition?

Eklavya Sarkar, Pavel Korshunov, Laurent Colbois +1

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 applicat…

cs.CV2020★ 8 cited

Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks

Eklavya Sarkar, Pavel Korshunov, Laurent Colbois +1

Morphing attacks is 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 applicati…

cs.CV2020★ 11 cited

Deepfake detection: humans vs. machines

Pavel Korshunov, Sébastien Marcel

Deepfake videos, where a person's face is automatically swapped with a face of someone else, are becoming easier to generate with more realistic results. In response to the threat…

eess.AS2019

The Speed Submission to DIHARD II: Contributions & Lessons Learned

Md Sahidullah, Jose Patino, Samuele Cornell +11

This paper describes the speaker diarization systems developed for the Second DIHARD Speech Diarization Challenge (DIHARD II) by the Speed team. Besides describing the system, whic…

eess.AS2019

pyannote.audio: neural building blocks for speaker diarization

Hervé Bredin, Ruiqing Yin, Juan Manuel Coria +7

We introduce pyannote.audio, an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it provides a set of trainable end-to-en…