Diffuse or Confuse: A Diffusion Deepfake Speech Dataset
<|reference_start|>Diffuse or Confuse: A Diffusion Deepfake Speech Dataset: Advancements in artificial intelligence and machine learning have significantly improved synthetic speech generation. This paper explores diffusion models, a novel method for creating realistic synthetic speech. We create a diffusion dataset using available tools and pretrained models. Additionally, this study assesses the quality of diffusion-generated deepfakes versus non-diffusion ones and their potential threat to current deepfake detection systems. Findings indicate that the detection of diffusion-based deepfakes is generally comparable to non-diffusion deepfakes, with some variability based on detector architecture. Re-vocoding with diffusion vocoders shows minimal impact, and the overall speech quality is comparable to non-diffusion methods.<|reference_end|>
@article{firc2024diffuse,
    title={Diffuse or Confuse: A Diffusion Deepfake Speech Dataset},
    author={Anton Firc, Kamil Malinka, Petr Han'av{c}ek},
    journal={arXiv preprint arXiv:2410.06796},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06796},
    primaryClass={cs.CR cs.AI cs.LG cs.SD}
}