David Doermann of University at Buffalo

The computer vision community has created a technology which unfortunately is getting more bad press then it is good. In 2014, the first GANS paper was able to automatically generate very low resolutions of faces of people which never existed, from a random latent distribution. Although the technology was impressive because it was automated, it was nowhere near as good as what could be done with the simple photo editor. In the same year DARPA started the media forensics program to combat the proliferation of edited images and video that was benign generated by our adversaries. Although DARPA envisioned the development automated technologies, no one thought they would evolve so fast. Five years later the technology has progressed to the point where even a novice can modify full videos, i.e. DeepFakes, and generate new content of people and scenes that never existed, overnight using commodity hardware. Recently the US government has become increasingly concerned about the real dangers of the use of “DeepFakes” technologies from both a national security and a misinformation point of view. To this end, it is important for academia, industry and the government to come together to apply technologies, develop policies that put pressure on service providers, and educate the public before we get to the point where “seeing is believing” is a thing of the past. In this talk I will cover some of the primary efforts in applying counter manipulation detection technology, the challenges we face with current policy in the United States. While technological solutions are still a number of years away, we need a comprehensive approach to deal with this problem.

Biography:

Dr. David Doermann is a Professor of Empire Innovation and the Director of the Artificial Intelligence Institute the University at Buffalo (UB). Prior to coming to UB he was a Program Manager with the Information Innovation Office at the Defense Advanced Research Projects Agency (DARPA) where he developed, selected and oversaw research and transition funding in the areas of computer vision, human language technologies and voice analytics. From 1993 to 2018, David was a member of the research faculty at the University of Maryland, College Park. In his role in the Institute for Advanced Computer Studies, he served as Director of the Laboratory for Language and Media Processing, and as an adjunct member of the graduate faculty for the Department of Computer Science and the Department of Electrical and Computer Engineering. He and his group of researchers focus on many innovative topics related to analysis and processing of document images and video including triage, visual indexing and retrieval, enhancement and recognition of both textual and structural components of visual media. David has over 250 publications in conferences and journals, is a fellow of the IEEE and IAPR, has numerous awards including an honorary doctorate from the University of Oulu, Finland and is a founding Editor-in-Chief of the International Journal on Document Analysis and Recognition.

Andy Gallagher of Google

Biography:

I joined Google in 2014. Previously, I was a Visiting Research Scientist at Cornell University's School of Electrical and Computer Engineering, beginning in June 2012. I earned the Ph.D. degree in electrical and computer engineering from Carnegie Mellon University in 2009, advised by Prof. Tsuhan Chen. I received an M.S. degree from Rochester Institute of Technology, and the B.S. degree from Geneva College, both in electrical engineering. I worked for the Eastman Kodak Company for over a decade during the fascinating transition from chemical to digital imaging, initially developing image enhancement algorithms for digital photofinishing. These algorithms were shipped under the trade name "Kodak Perfect Touch" in photo printing mini-labs, and millions of digital cameras, and enhanced many billions of images. I enjoy working on tough and interesting problems.