Generation and Detection of Sign Language Deepfakes -- A Linguistic and Visual Analysis
<|reference_start|>Generation and Detection of Sign Language Deepfakes -- A Linguistic and Visual Analysis: A question in the realm of deepfakes is slowly emerging pertaining to whether we can go beyond facial deepfakes and whether it would be beneficial to society. Therefore, this research presents a positive application of deepfake technology in upper body generation, while performing sign-language for the Deaf and Hard of Hearing (DHoH) community. The resulting videos are later vetted with a sign language expert. This is particularly helpful, given the intricate nature of sign language, a scarcity of sign language experts, and potential benefits for health and education. The objectives of this work encompass constructing a reliable deepfake dataset, evaluating its technical and visual credibility through computer vision and natural language processing models, and assessing the plausibility of the generated content. With over 1200 videos, featuring both previously seen and unseen individuals for the generation model, using the help of a sign language expert, we establish a deepfake dataset in sign language that can further be utilized to detect fake videos that may target certain people of determination.<|reference_end|>
@article{naeem2024generation,
    title={Generation and Detection of Sign Language Deepfakes -- A Linguistic and
  Visual Analysis},
    author={Shahzeb Naeem, Muhammad Riyyan Khan, Usman Tariq, Abhinav Dhall,
  Carlos Ivan Colon and Hasan Al-Nashash},
    journal={arXiv preprint arXiv:2404.01438},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01438},
    primaryClass={cs.CV cs.AI}
}