Speaker: David Doermann, University at Buffalo
(Talk virtually over zoom; note: talk will be recorded)
Abstract:
The computer vision community has created a technology that, unfortunately, is getting more bad press than good. In 2014, the first GANS paper automatically generated very low resolutions of faces of people that 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 videos generated by our adversaries. Although DARPA envisioned the development of 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 using “DeepFakes” technologies from both national security and misinformation points of view. To this end, academia, industry, and the government needs 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. I will discuss how we are extending existing technology to deal with the problems of detecting GAN generated content and highlighting inconsistencies between the text, audio, image, and video content in heterogeneous media “assets.”
Bio: Dr. David Doermann is a Professor of Empire Innovation and the Director of the Artificial Intelligence and Data Science Institute at 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 the 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.