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Sunday, January 15, 2023

Machine Generated Text, Threat Models,

Part of a current survey of mine.  intro below, more at the link

Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods


Advances in natural language generation (NLG) have resulted in machine generated text that is increasingly difficult to distinguish from human authored text. Powerful open-source models are freely available, and user-friendly tools democratizing access to generative models are proliferating. The great potential of state-of-the-art NLG systems is tempered by the multitude of avenues for abuse. Detection of machine generated text is a key countermeasure for reducing abuse of NLG models, with significant technical challenges and numerous open problems. We provide a survey that includes both 1) an extensive analysis of threat models posed by contemporary NLG systems, and 2) the most complete review of machine generated text detection methods to date. This survey places machine generated text within its cybersecurity and social context, and provides strong guidance for future work addressing the most critical threat models, and ensuring detection systems themselves demonstrate trustworthiness through fairness, robustness, and accountability. CCS Concepts: • Computing methodologies → Machine learning approaches; Neural networks; Natural language generation; • Security and privacy → Human and societal aspects of security and privacy.

Additional Key Words and Phrases: machine learning, artificial intelligence, neural networks, trustworthy AI, natural language generation, machine generated text, transformer, text generation, threat modeling, cybersecurity, disinformation   ... ' 

See also a Schneier summary, with thoughtful added comments. 

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