17 citations · 37 across the 22 of their papers we have counts for
7 papers · 1 filter
Circumventing shortcuts in audio-visual deepfake detection datasets with unsupervised learning
Stefan Smeu, Dragos-Alexandru Boldisor, Dan Oneata +1
Good datasets are essential for developing and benchmarking any machine learning system. Their importance is even more extreme for safety critical applications such as deepfake det…
DeCLIP: Decoding CLIP representations for deepfake localization
Stefan Smeu, Elisabeta Oneata, Dan Oneata
Generative models can create entirely new images, but they can also partially modify real images in ways that are undetectable to the human eye. In this paper, we address the chall…
Improved Visually Prompted Keyword Localisation in Real Low-Resource Settings
Leanne Nortje, Dan Oneata, Gabriel Pirlogeanu +1
Given an image query, visually prompted keyword localisation (VPKL) aims to find occurrences of the depicted word in a speech collection. This can be useful when transcriptions are…
Easy, Interpretable, Effective: openSMILE for voice deepfake detection
Octavian Pascu, Dan Oneata, Horia Cucu +1
In this paper, we demonstrate that attacks in the latest ASVspoof5 dataset -- a de facto standard in the field of voice authenticity and deepfake detection -- can be identified wit…
WavLM model ensemble for audio deepfake detection
David Combei, Adriana Stan, Dan Oneata +1
Audio deepfake detection has become a pivotal task over the last couple of years, as many recent speech synthesis and voice cloning systems generate highly realistic speech samples…
Translating speech with just images
Dan Oneata, Herman Kamper
Visually grounded speech models link speech to images. We extend this connection by linking images to text via an existing image captioning system, and as a result gain the ability…