34 citations · 56 across the 5 of their papers we have counts for
5 papers
Embedding Attack Project (Work Report)
Jiameng Pu, Zafar Takhirov
This report summarizes all the MIA experiments (Membership Inference Attacks) of the Embedding Attack Project, including threat models, experimental setup, experimental results, fi…
Deepfake Text Detection: Limitations and Opportunities
Jiameng Pu, Zain Sarwar, Sifat Muhammad Abdullah +5
Recent advances in generative models for language have enabled the creation of convincing synthetic text or deepfake text. Prior work has demonstrated the potential for misuse of d…
Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models
Neal Mangaokar, Jiameng Pu, Parantapa Bhattacharya +2
Advances in deep neural networks (DNNs) have shown tremendous promise in the medical domain. However, the deep learning tools that are helping the domain, can also be used against…
T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification
Ahmadreza Azizi, Ibrahim Asadullah Tahmid, Asim Waheed +5
Deep Neural Network (DNN) classifiers are known to be vulnerable to Trojan or backdoor attacks, where the classifier is manipulated such that it misclassifies any input containing…
Deepfake Videos in the Wild: Analysis and Detection
Jiameng Pu, Neal Mangaokar, Lauren Kelly +5
AI-manipulated videos, commonly known as deepfakes, are an emerging problem. Recently, researchers in academia and industry have contributed several (self-created) benchmark deepfa…