5 papers
Foundation Models are Implicit Deepfake Detectors
Stefan Smeu, Dragos-Alexandru Boldisor, Elisabeta Oneata +1
Pretrained self-supervised representations have emerged as a core component of current deepfake detection methods, yet it remains unclear which of their properties make real and fa…
Investigating self-supervised representations for audio-visual deepfake detection
Dragos-Alexandru Boldisor, Stefan Smeu, Dan Oneata +1
Self-supervised representations excel at many vision and speech tasks, but their potential for audio-visual deepfake detection remains underexplored. Unlike prior work that uses th…
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…
Towards generalisable and calibrated synthetic speech detection with self-supervised representations
Octavian Pascu, Adriana Stan, Dan Oneata +2
Generalisation -- the ability of a model to perform well on unseen data -- is crucial for building reliable deepfake detectors. However, recent studies have shown that the current…