29 citations · 60 across the 8 of their papers we have counts for
8 papers
Advancing Reliable Synthetic Video Detection: Insights from the SAFE Challenge
Kirill Trapeznikov, Gabriel Mancino-Ball, Jonathan Li +9
The proliferation of generative video technologies has intensified the need for reliable methods to detect and characterize synthetic media. To address this challenge, we organized…
Defending Against Social Engineering Attacks in the Age of LLMs
Lin Ai, Tharindu Kumarage, Amrita Bhattacharjee +12
The proliferation of Large Language Models (LLMs) poses challenges in detecting and mitigating digital deception, as these models can emulate human conversational patterns and faci…
Comprehensive Dataset of Synthetic and Manipulated Overhead Imagery for Development and Evaluation of Forensic Tools
Brandon B. May, Kirill Trapeznikov, Shengbang Fang +1
We present a first of its kind dataset of overhead imagery for development and evaluation of forensic tools. Our dataset consists of real, fully synthetic and partially manipulated…
Stylometric Detection of AI-Generated Text in Twitter Timelines
Tharindu Kumarage, Joshua Garland, Amrita Bhattacharjee +3
Recent advancements in pre-trained language models have enabled convenient methods for generating human-like text at a large scale. Though these generation capabilities hold great…
Comprehensive Dataset of Face Manipulations for Development and Evaluation of Forensic Tools
Brian DeCann, Kirill Trapeznikov
Digital media (e.g., photographs, video) can be easily created, edited, and shared. Tools for editing digital media are capable of doing so while also maintaining a high degree of…
Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction
Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama
We study the problem of reducing test-time acquisition costs in classification systems. Our goal is to learn decision rules that adaptively select sensors for each example as neces…
Sensor Selection by Linear Programming
Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama
We learn sensor trees from training data to minimize sensor acquisition costs during test time. Our system adaptively selects sensors at each stage if necessary to make a confident…
Multi-Stage Classifier Design
Kirill Trapeznikov, Venkatesh Saligrama, David Castanon
In many classification systems, sensing modalities have different acquisition costs. It is often {\it unnecessary} to use every modality to classify a majority of examples. We stud…