( en | de | ru )

 

Disambiguation of "Mahmood, Kaleel"

Author of the publication

copydeleteadd this publication to your clipboard
Besting the Black-Box: Barrier Zones for Adversarial Example Defense.

, , , , and . IEEE Access, (2022)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Athar Mahmood

Talat Mahmood

Mahmood Saiyadpour

Mahmood Haiaty

Mahmood Mahmood Ali Barzani

 

Other publications of authors with the same name

MXPUF: Secure PUF Design against State-of-the-art Modeling Attacks., , , , and . IACR Cryptology ePrint Archive, (2017)BUZz: BUffer Zones for defending adversarial examples in image classification., , , , and . CoRR, (2019)Moving target defense for Internet of Things using context aware code partitioning and code diversification., and . WF-IoT, page 329-330. IEEE Computer Society, (2016)Beware the Black-Box: On the Robustness of Recent Defenses to Adversarial Examples., , , and . Entropy, 23 (10): 1359 (2021)Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks., , , and . CoRR, (2021)Analyzing and Defending against Membership Inference Attacks in Natural Language Processing Classification., , , , , , , and . IEEE Big Data, page 5823-5832. IEEE, (2022)Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration., , , , , , , , , and . DAC, page 1-6. IEEE, (2023)The Interpose PUF: Secure PUF Design against State-of-the-art Machine Learning Attacks., , , , , and . IACR Cryptology ePrint Archive, (2018)Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks., , , and . IEEE Access, (2022)AQ2PNN: Enabling Two-party Privacy-Preserving Deep Neural Network Inference with Adaptive Quantization., , , , , , , , and . MICRO, page 628-640. ACM, (2023)