Abstract: The past few years have seen a startling and troubling rise in the fake-news phenomena in which everyone from individuals to state-sponsored entities can produce and distribute mis-information. The implications of fake news range from a mis-informed 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 and videos, making the fake-news phenomena even more powerful and dangerous. I will provide a broad overview of the field of digital image forensics and how these techniques — working in the absence of digital watermarks or signatures — can begin to return some trust to the images and videos that we see every day.

Short Bio: I am a Professor at the University of California, Berkeley with a joint appointment in Electrical Engineering & Computer Science and the School of Information. My research focuses on digital forensics, image analysis, and human perception. I received my undergraduate degree in Computer Science and Applied Mathematics from the University of Rochester in 1989, my M.S. in Computer Science from SUNY Albany, and my 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, I joined the faculty at Dartmouth College in 1999 where I remained until 2019. I am the recipient of an Alfred P. Sloan Fellowship, a John Simon Guggenheim Fellowship, and am a Fellow of the National Academy of Inventors.