DLR: Adversarial examples detection and label recovery for deep neural networks.
Pattern Recognit. Lett., 2025
Rethinking the validity of perturbation in single-step adversarial training.
Pattern Recognit., 2025
Feature-aware transferable adversarial attacks against image classification.
Appl. Soft Comput., 2024
Scalable Attribution of Adversarial Attacks via Multi-Task Learning.
CoRR, 2023
Attribution of Adversarial Attacks via Multi-task Learning.
Proceedings of the Neural Information Processing - 30th International Conference, 2023
Advancing Example Exploitation Can Alleviate Critical Challenges in Adversarial Training.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023
(AD)2: Adversarial domain adaptation to defense with adversarial perturbation removal.
Pattern Recognit., 2022
Transferable Interpolated Adversarial Attack with Random-Layer Mixup.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2022
Ensemble adversarial black-box attacks against deep learning systems.
Pattern Recognit., 2020
Is It Time to Redefine the Classification Task for Deep Neural Networks?
CoRR, 2020
A Way to Explore the Lower Bound of Adversarial Perturbation.
Proceedings of the 2020 IEEE International Conference on Big Data and Smart Computing, 2020
Adversary resistant deep neural networks via advanced feature nullification.
Knowl. Based Syst., 2019
Adversarial Training Based Feature Selection.
Proceedings of the Science of Cyber Security - Second International Conference, 2019
Delving into Diversity in Substitute Ensembles and Transferability of Adversarial Examples.
Proceedings of the Neural Information Processing - 25th International Conference, 2018