all InfoSec news
Deepfake Text Detection: Limitations and Opportunities. (arXiv:2210.09421v1 [cs.CR])
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 …
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
SOC 2 Manager, Audit and Certification
@ Deloitte | US and CA Multiple Locations
Information Security Engineers
@ D. E. Shaw Research | New York City
Intermediate Security Engineer, (Incident Response, Trust & Safety)
@ GitLab | Remote, US
Journeyman Cybersecurity Triage Analyst
@ Peraton | Linthicum, MD, United States
Project Manager II - Compliance
@ Critical Path Institute | Tucson, AZ, USA
Junior System Engineer (m/w/d) Cyber Security 1
@ Deutsche Telekom | Leipzig, Deutschland