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Illegal Crypto Transaction Volumes Hit All-Time High
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Threats of Machine-Generated Text
With the release of ChatGPT, I’ve read many random articles about this or that threat from the technology. This paper is a good survey of the field: what the threats are, how we might detect machine-generated text, directions for future research. It’s a solid grounding amongst all of the hype.
Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Abstract: 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.
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Twitter: Leak of 200 Million Accounts Not Due to Historic Bug
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Quarter of UK SMBs Hit by Ransomware in 2022
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Google Chrome ‘SymStealer’ Vulnerability Could Affect 2.5 Billion Users
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Cyber-Threat Actors Tailoring Attacks to Key Sectors
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Telegram Bot Abuse For Phishing Increased By 800% in 2022
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The Guardian Confirms UK Members’ Data Was Accessed in Ransomware Attack
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S3 Ep117: The crypto crisis that wasn’t (and farewell forever to Win 7) [Audio + Text]
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