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 produce and distribute mis-information, which is then widely promoted and disseminated on social media. 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 start by providing a broad overview of the field of image and video forensics and then I will describe in detail a suite of image forensic techniques that explicitly detect inconsistencies in JPEG coding artifacts.
I am the Albert Bradley 1915 Third Century Professor of Computer Science at Dartmouth. 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 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 in 1999. I am the recipient of an Alfred P. Sloan Fellowship, a John Simon Guggenheim Fellowship, and I am a Fellow of the National Academy of Inventors.