Oct. 19, 2022, 2:20 a.m. | Jiameng Pu, Zain Sarwar, Sifat Muhammad Abdullah, Abdullah Rehman, Yoonjin Kim, Parantapa Bhattacharya, Mobin Javed, Bimal Viswanath

cs.CR updates on arXiv.org arxiv.org

Recent advances in generative models for language have enabled the creation
of convincing synthetic text or deepfake text. Prior work has demonstrated the
potential for misuse of deepfake text to mislead content consumers. Therefore,
deepfake text detection, the task of discriminating between human and
machine-generated text, is becoming increasingly critical. Several defenses
have been proposed for deepfake text detection. However, we lack a thorough
understanding of their real-world applicability. In this paper, we collect
deepfake text from 4 online services …

deepfake detection text

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