With the prevalence of social media platforms, media shared on the Internet can reach millions of people in a short time. Sheer amounts of media available on the Internet enable many different computer vision applications. However, at the same time, people can easily share a tampered media for malicious goals such as creating panic or distorting public opinions with little effort.

We first present an image geo-localization framework for extracting fine-grained location information (i.e. business venues) from images. Our framework utilizes the information available from social media websites such as Instagram and Yelp to extract a set of location-related concepts. Secondly, to make a robust system, we address the metadata tampering detection problem, detecting the discrepancy between the images and its associated metadata such as GPS and timestamp. Third, we present a generative model to generate realistic image compositing using adversarial learning, which can be used to further improve the image tampering detection model. Finally, we propose an object-based provenance approach to address the content manipulation problem in media forensics.

 

                           Chair:              Dr. Larry Davis
                          Dean's rep:      Dr. Rama Chellappa
                          Members:        Dr. David Jacobs
                                                    Dr. Yaser Yacoob
                                               Dr. Tom Goldstein