activity
20172022
most citedProvenance Filtering for Multimedia Phylogeny

1 citations · 2 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV2020

The Mertens Unrolled Network (MU-Net): A High Dynamic Range Fusion Neural Network for Through the Windshield Driver Recognition

Max Ruby, David S. Bolme, Joel Brogan +7

Face recognition of vehicle occupants through windshields in unconstrained environments poses a number of unique challenges ranging from glare, poor illumination, driver pose and m…

cs.CV2020

Automatic Discovery of Political Meme Genres with Diverse Appearances

William Theisen, Joel Brogan, Pamela Bilo Thomas +4

Forms of human communication are not static -- we expect some evolution in the way information is conveyed over time because of advances in technology. One example of this phenomen…

cs.CV2019

Dynamic Spatial Verification for Large-Scale Object-Level Image Retrieval

Joel Brogan, Aparna Bharati, Daniel Moreira +4

Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks. Researchers working onContent-Ba…

cs.CV2018

Beyond Pixels: Image Provenance Analysis Leveraging Metadata

Aparna Bharati, Daniel Moreira, Joel Brogan +5

Creative works, whether paintings or memes, follow unique journeys that result in their final form. Understanding these journeys, a process known as "provenance analysis", provides…

cs.CV2018

Image Provenance Analysis at Scale

Daniel Moreira, Aparna Bharati, Joel Brogan +6

Prior art has shown it is possible to estimate, through image processing and computer vision techniques, the types and parameters of transformations that have been applied to the c…

cs.IR20171 cited

Provenance Filtering for Multimedia Phylogeny

Allan Pinto, Daniel Moreira, Aparna Bharati +5

Departing from traditional digital forensics modeling, which seeks to analyze single objects in isolation, multimedia phylogeny analyzes the evolutionary processes that influence d…