Neal Mangaokar
Neal Mangaokar
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T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification
A Azizi, I Tahmid, A Waheed, N Mangaokar, J Pu, M Javed, CK Reddy, ...
30th USENIX Security Symposium (USENIX Security 2021), 2021
Deepfake Videos in the Wild: Analysis and Detection
J Pu (co-lead), N Mangaokar (co-lead), L Kelly, P Bhattacharya, ...
28th ACM World Wide Web Conference (WWW 2021), 2021
NoiseScope: Detecting Deepfake Images in a Blind Setting
J Pu, N Mangaokar, B Wang, C K. Reddy, B Viswanath
36th ACM Annual Computer Security Applications Conference (ACSAC 2020), 2020
Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models
N Mangaokar, J Pu, P Bhattacharya, CK Reddy, B Viswanath
5th IEEE European Symposium on Security and Privacy (EuroS&P 2020), 139-157, 2020
GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems
R Feng, N Mangaokar, J Chen, E Fernandes, S Jha, A Prakash
2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P), 664-683, 2022
Dispelling misconceptions and characterizing the failings of deepfake detection
N Mangaokar, A Prakash
IEEE Security & Privacy 20 (2), 61-67, 2021
Towards Adversarially Robust Deepfake Detection: An Ensemble Approach
A Hooda, N Mangaokar, R Feng, K Fawaz, S Jha, A Prakash
arXiv preprint arXiv:2202.05687, 2022
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