How to Defend Against Weaponized Generative AI

Abstract: 

As generative AI advances at a lightning-fast pace, there is an ever-growing concern over how this technology can be and is being misused to spread misinformation and erode public trust. Deepfakes — highly realistic fake videos, images, and audio generated by AI — pose a major threat as potential tools of deception and propaganda across a variety of mediums. Defending against weaponized uses of generative AI is thus an urgent challenge, one that is necessary for the preservation of a harmonious society.

This session will provide an overview of the deepfake landscape and discuss emerging techniques for detection and prevention. We will explore leading edge tools and research on deepfake detection and prevention, including methods that analyze artifacts and inconsistencies introduced during generation to identify manipulation. Going beyond detection, we will also cover proactive technical and policy interventions, such as digital provenance techniques and content authentication frameworks. Broader societal resilience strategies will also be discussed.

With democratization of AI lowering barriers and cost to creation of convincing deepfakes, responsible governance of generative technologies is needed more than ever. Participants will leave this session with an understanding of deepfake risks, leading edge detection methods in development, and a roadmap toward fostering public awareness and policy solutions. By highlighting countermeasures across technology, media literacy, and regulation, this talk will provide actionable intelligence for defending against the misuse of AI to corrupt information ecosystems.

Bio: 

Jacob Seidman leads the Core AI team at Reality Defender, where he investigates strategies for robust model performance and bias mitigation. His previous research includes work on machine learning for functional data, physics-informed machine learning, and robust optimization. He holds a Ph.D. in applied mathematics and computational science from the University of Pennsylvania, and an A.B. in mathematics from Harvard.

Open Data Science

 

 

 

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Cambridge, MA 02142
info@odsc.com

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