Hany Farid

Digital Forensics Pioneer, UC Berkeley

Hany Farid is a Professor at the UC Berkeley with a joint appointment in Electrical Engineering & Computer Science and the School of Information. His research focuses on digital forensics, image analysis, and human perception. He received his undergraduate degree in Computer Science and Applied Mathematics from the University of Rochester in 1989, his M.S. in Computer Science from SUNY Albany, and his Ph.D. in Computer Science from the University of Pennsylvania in 1997. Following a two-year post-doctoral fellowship in Brain and Cognitive Sciences at MIT, he joined the faculty at Dartmouth College in 1999 where he remained until 2019. He is the recipient of an Alfred P. Sloan Fellowship, a John Simon Guggenheim Fellowship, and is a Fellow of the National Academy of Inventors.



Spark + AI Summit 2020: Thursday Afternoon KeynotesSummit 2020

Kim Hazelwood - Deep Learning: It’s Not All About Recognizing Cats and Dogs (Facebook) - 5:22 Hany Farid - Creating, Weaponizing, and Detecting Deep Fakes (UC Berkeley) - 24:40

Deep Learning: It’s Not All About Recognizing Cats and Dogs
Kim Hazelwood

Based on a recent blog post and paper, this talk would focus on the fact that recommendation systems tend to be underinvested in the overall research community, and why that’s problematic.

Creating, Weaponizing, and Detecting Deep Fakes
Hany Farid

The past few years have seen a startling and troubling rise in the fake-news phenomena in which everyone from individuals to nation-sponsored entities can produce and distribute misinformation. The implications of fake news range from a misinformed public to an existential threat to democracy, and horrific violence. At the same time, recent and rapid advances in machine learning are making it easier than ever to create sophisticated and compelling fake images. videos, and audio recordings, making the fake-news phenomena even more powerful and dangerous. I will provide an overview of the creation of these so-called deep-fakes, and I will describe emerging techniques for detecting them.