Search Results for author:
Found 9 papers, 4 papers with code
MONet: Multi-scale Overlap Network for Duplication Detection in Biomedical Images
Manipulation of biomedical images to misrepresent experimental results has plagued the biomedical community for a while.
BioFors: A Large Biomedical Image Forensics Dataset
Our results and analysis show that existing algorithms developed on common computer vision datasets are not robust when applied to biomedical images, validating that more research is required to address the unique challenges of biomedical image forensics.
MEG: Multi-Evidence GNN for Multimodal Semantic Forensics
The problem setup requires algorithms to perform multimodal semantic forensics to authenticate a query multimedia package using a reference dataset of potentially related packages as evidences.
CORD19STS: COVID-19 Semantic Textual Similarity Dataset
To overcome this gap, we introduce CORD19STS dataset which includes 13, 710 annotated sentence pairs collected from COVID-19 open research dataset (CORD-19) challenge.
Recurrent Convolutional Strategies for Face Manipulation Detection in Videos
The spread of misinformation through synthetically generated yet realistic images and videos has become a significant problem, calling for robust manipulation detection methods.
Policy Design for Active Sequential Hypothesis Testing using Deep Learning
Deep learning can be used as a tool for designing better heuristics in such problems.
Deep Multimodal Image-Repurposing Detection
Nefarious actors on social media and other platforms often spread rumors and falsehoods through images whose metadata (e. g., captions) have been modified to provide visual substantiation of the rumor/falsehood.
Implicit Language Model in LSTM for OCR
Neural networks have become the technique of choice for OCR, but many aspects of how and why they deliver superior performance are still unknown.
Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text
In this paper, we present a novel deep learning-based approach for assessing the semantic integrity of multimedia packages containing images and captions, using a reference set of multimedia packages.
