R<sup>2</sup>S100K: Road-Region Segmentation Dataset for Semi-supervised Autonomous Driving in the Wild.
Int. J. Comput. Vis., February, 2025
Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally.
IEEE Trans. Artif. Intell., September, 2024
Secure and Trustworthy Artificial Intelligence-extended Reality (AI-XR) for Metaverses.
ACM Comput. Surv., July, 2024
Consistent Valid Physically-Realizable Adversarial Attack Against Crowd-Flow Prediction Models.
IEEE Trans. Intell. Transp. Syst., June, 2024
Adversarially Guided Stateful Defense Against Backdoor Attacks in Federated Deep Learning.
CoRR, 2024
Con-Detect: Detecting adversarially perturbed natural language inputs to deep classifiers through holistic analysis.
Comput. Secur., September, 2023
R2S100K: Road-Region Segmentation Dataset For Semi-Supervised Autonomous Driving in the Wild.
CoRR, 2023
Membership Inference Attacks on DNNs using Adversarial Perturbations.
CoRR, 2023
Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data.
CoRR, 2023
Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study.
Comput. Secur., 2023
Tamp-X: Attacking explainable natural language classifiers through tampered activations.
Comput. Secur., 2022
All Your Fake Detector are Belong to Us: Evaluating Adversarial Robustness of Fake-News Detectors Under Black-Box Settings.
IEEE Access, 2021
SSCNets: Robustifying DNNs using Secure Selective Convolutional Filters.
IEEE Des. Test, 2020
HaS-Nets: A Heal and Select Mechanism to Defend DNNs Against Backdoor Attacks for Data Collection Scenarios.
CoRR, 2020
FaDec: A Fast Decision-based Attack for Adversarial Machine Learning.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020
RED-Attack: Resource Efficient Decision based Attack for Machine Learning.
CoRR, 2019
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks.
Proceedings of the 25th IEEE International Symposium on On-Line Testing and Robust System Design, 2019
SSCNets: A Selective Sobel Convolution-based Technique to Enhance the Robustness of Deep Neural Networks against Security Attacks.
CoRR, 2018