The Current State of AI Generated Text Detection
New articles are being released frequently discussing enhancements in language models. Models like GPT2 and BERT are able to mimic human text where even humans have a hard time distinguishing that the text was generated by a machine. As these models continue to improve, the ability for them to be used in a malicious way is increasing. Bad actors could use these models to automate phishing emails, impersonate a celebrity on twitter, or spread fake news. It is important that security researchers look to develop methods to detect machine generated text as a defense against these use cases. GROVER and GLTR are two state of the art frameworks in machine generated text detection. McAfee is working to utilize these frameworks as a way to detect machine generated text.
Dr. Sherin Mathews is a Senior Data Scientist within the Office of the CTO for McAfee. In this role she creates and develops new machine learning models to improve and increase the effectiveness of cybersecurity products. She is currently working on detection technologies for DeepFake and machine generated text and is deeply passionate about Explainable AI, Ethical AI, combating DeepFakes & misinformation.
Sherin is a repeated and requested industry speaker on her research papers in areas of signal and image processing, machine Learning, computer vision, artificial intelligence and cybersecurity. Prior to her role at McAfee, Sherin held research positions at Canon Inc. and Intel Corporation. She has a BSEE degree, with honors, from the University of Mumbai and a MS in Electrical and Computer Engineering focused in Signal Processing and Wireless Communication from State University of New York, College at Buffalo. Additionally Sherin has a MSEE in Software Engineering & Signal Processing and a PhD in Machine Learning and Signal Processing from the University of Delaware. She is a past recipient of the University of Delaware Professional Development Award and received ninth place in the prestigious IEEE GRSS Data Fusion Contest.
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