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Kenneth Co
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Byzantine-robust Federated Machine Learning Through Adaptive Model Averaging
L Muñoz-González, KT Co, EC Lupu
arXiv preprint arXiv:1909.05125, 2019
682019
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks
KT Co, L Munoz Gonzalez, S De Maupeou, E Lupu
26th ACM SIGSAC Conference on Computer and Communications Security (CCS 2019), 2019
262019
Bayesian Optimization for Black-Box Evasion of Machine Learning Systems
KT Co
Imperial College London, 2017
8*2017
Object Removal Attacks on LiDAR-based 3D Object Detectors
Z Hau, KT Co, S Demetriou, EC Lupu
NDSS 2021: Automotive and Autonomous Vehicle Security (AutoSec), 2021
62021
Jacobian Regularization for Mitigating Universal Adversarial Perturbations
KT Co, DM Rego, EC Lupu
30th International Conference on Artificial Neural Networks (ICANN 2021), 2021
42021
Sensitivity of Deep Convolutional Networks to Gabor Noise
KT Co, L Muñoz-González, EC Lupu
ICML 2019: On Identifying and Understanding Deep Learning Phenomena, 2019
42019
Universal Adversarial Robustness of Texture and Shape-Biased Models
KT Co, L Muñoz-González, L Kanthan, B Glocker, EC Lupu
28th IEEE International Conference on Image Processing (ICIP 2021), 2019
32019
Robustness and Transferability of Universal Attacks on Compressed Models
AG Matachana, KT Co, L Muñoz-González, D Martinez, EC Lupu
AAAI 2021: Towards Robust, Secure, and Efficient Machine Learning, 2020
22020
Challenges and Advances in Adversarial Machine Learning
L Muñoz-González, J Carnerero-Cano, KT Co, EC Lupu
Resilience and Hybrid Threats: Security and Integrity for the Digital World …, 2019
22019
Jacobian Ensembles Improve Robustness Trade-offs to Adversarial Attacks
KT Co, D Martinez-Rego, Z Hau, EC Lupu
31st International Conference on Artificial Neural Networks (ICANN 2022), 2022
2022
Real-time Detection of Practical Universal Adversarial Perturbations
KT Co, L Muñoz-González, L Kanthan, EC Lupu
arXiv preprint arXiv:2105.07334, 2021
2021
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