Jul 11, 2022 · This paper investigates the susceptibility of continually learned tasks, including current and previously acquired tasks, to adversarial attacks ...
This work investigates advanced methods for improving the resilience of machine learning models against adversarial attacks. Ensuring that these models can ...
2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022. 13, 2022. Susceptibility of continual learning against adversarial attacks. H Khan ...
Jul 11, 2022 · This paper presents a comprehensive study of the susceptibility of the continually learned tasks (including both current and previously learned ...
Jul 3, 2023 · the performance on each task. In essence, current continual learning methods are susceptible to attacks on previous tasks. We demonstrate ...
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Deep neural networks have demonstrated susceptibility to adversarial attacks. Adversarial defense techniques of- ten focus on one-shot setting to maintain ...
Mar 17, 2024 · Conversely, mixup training aims to increase the model's flexibility in response to linear transformations between continuous embeddings of the ...
Presents class-wise vulnerability of the EWC online [16] against the FGSM and PGD [38, 39] adversarial attacks under Domain-IL setting of continual learning.
Nov 6, 2024 · Adversarial attacks in machine learning (ML) represent a significant threat to the integrity and reliability of AI systems.
We conduct a thorough ablation study over each key component as well as a hyperparameter sensitivity analysis to demonstrate the effectiveness and robustness of ...