Technical Report on the CleverHans v2.1.0 Adversarial Examples Library N Papernot, F Faghri, N Carlini, I Goodfellow, R Feinman, A Kurakin, ... arXiv preprint arXiv:1610.00768v6 10, 2018 | 706* | 2018 |
Vulnerability of deep reinforcement learning to policy induction attacks V Behzadan, A Munir Machine Learning and Data Mining in Pattern Recognition: 13th International …, 2017 | 316 | 2017 |
Security and privacy issues in intelligent transportation systems: Classification and challenges D Hahn, A Munir, V Behzadan IEEE Intelligent Transportation Systems Magazine 13 (1), 181-196, 2019 | 132 | 2019 |
Whatever does not kill deep reinforcement learning, makes it stronger V Behzadan, A Munir arXiv preprint arXiv:1712.09344, 2017 | 72 | 2017 |
Adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles V Behzadan, A Munir IEEE Intelligent Transportation Systems Magazine 13 (2), 236-241, 2019 | 58 | 2019 |
Corpus and deep learning classifier for collection of cyber threat indicators in twitter stream V Behzadan, C Aguirre, A Bose, W Hsu 2018 IEEE International Conference on Big Data (Big Data), 5002-5007, 2018 | 51 | 2018 |
A novel approach for detection and ranking of trendy and emerging cyber threat events in twitter streams A Bose, V Behzadan, C Aguirre, WH Hsu Proceedings of the 2019 IEEE/ACM International Conference on Advances in …, 2019 | 35 | 2019 |
Mitigation of policy manipulation attacks on deep q-networks with parameter-space noise V Behzadan, A Munir Computer Safety, Reliability, and Security: SAFECOMP 2018 Workshops, ASSURE …, 2018 | 31 | 2018 |
Adversarial exploitation of policy imitation V Behzadan, W Hsu arXiv preprint arXiv:1906.01121, 2019 | 26 | 2019 |
The faults in our pi stars: Security issues and open challenges in deep reinforcement learning V Behzadan, A Munir arXiv preprint arXiv:1810.10369, 2018 | 25 | 2018 |
Founding the domain of AI forensics I Baggili, V Behzadan arXiv preprint arXiv:1912.06497, 2019 | 24 | 2019 |
A psychopathological approach to safety engineering in ai and agi V Behzadan, A Munir, RV Yampolskiy Computer Safety, Reliability, and Security: SAFECOMP 2018 Workshops, ASSURE …, 2018 | 24 | 2018 |
Sequential triggers for watermarking of deep reinforcement learning policies V Behzadan, W Hsu arXiv preprint arXiv:1906.01126, 2019 | 23 | 2019 |
Cyber-physical attacks on uas networks-challenges and open research problems V Behzadan arXiv preprint arXiv:1702.01251, 2017 | 16 | 2017 |
Sentimental liar: Extended corpus and deep learning models for fake claim classification B Upadhayay, V Behzadan 2020 IEEE International Conference on Intelligence and Security Informatics …, 2020 | 15 | 2020 |
Adaptive beam nulling in multihop ad hoc networks against a jammer in motion S Bhunia, V Behzadan, PA Regis, S Sengupta Computer Networks 109, 50-66, 2016 | 15 | 2016 |
Performance of adaptive beam nulling in multihop ad-hoc networks under jamming S Bhunia, V Behzadan, PA Regis, S Sengupta 2015 IEEE 17th International Conference on High Performance Computing and …, 2015 | 13 | 2015 |
Rl-based method for benchmarking the adversarial resilience and robustness of deep reinforcement learning policies V Behzadan, W Hsu Computer Safety, Reliability, and Security: SAFECOMP 2019 Workshops, ASSURE …, 2019 | 11 | 2019 |
Emergence of addictive behaviors in reinforcement learning agents V Behzadan, RV Yampolskiy, A Munir arXiv preprint arXiv:1811.05590, 2018 | 11 | 2018 |
Technical report on the cleverhans v2. 1.0 adversarial examples library. arXiv N Papernot, F Faghri, N Carlini, I Goodfellow, R Feinman, A Kurakin, ... arXiv preprint arXiv:1610.00768, 2018 | 11 | 2018 |