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1. Distributed Online Learning With Adversarial Participants In An Adversarial Environment

   https://doi.org/10.1109/ICASSP49357.2023.10095178  

   Dong, Xingrong ; Wu, Zhaoxian ; Ling, Qing ; Tian, Zhi ( June 2023 , IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))
2. Adversarial-HD: Hyperdimensional Computing Adversarial Attack Design for Secure Industrial Internet of Things

   https://doi.org/10.1145/3576914.3587484  

   Gungor, Onat ; Rosing, Tajana ; Aksanli, Baris ( May 2023 , Proceedings of Cyber-Physical Systems and Internet of Things Week 2023)
3. Efficient Adversarial Training With Transferable Adversarial Examples

   https://doi.org/10.1109/CVPR42600.2020.00126  

   Zheng, Haizhong ; Zhang, Ziqi ; Gu, Juncheng ; Lee, Honglak ; Prakash, Atul ( June 2020 , 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))

   null (Ed.)

   Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can re- quire orders of magnitude additional training time due to high cost of generating strong adversarial examples dur- ing training. In this paper, we first show that there is high transferability between models from neighboring epochs in the same training process, i.e., adversarial examples from one epoch continue to be adversarial in subsequent epochs. Leveraging this property, we propose a novel method, Adversarial Training with Transferable Adversarial Examples (ATTA), that can enhance the robustness of trained models and greatly improve the training efficiency by accumulating adversarial perturbations through epochs. Compared to state-of-the-art adversarial training methods, ATTA enhances adversarial accuracy by up to 7.2% on CIFAR10 and requires 12 ∼ 14× less training time on MNIST and CIFAR10 datasets with comparable model robustness. 

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4. Defense against Adversarial Attacks on Hybrid Speech Recognition System using Adversarial Fine-tuning with Denoiser

   https://doi.org/10.21437/Interspeech.2022-10977  

   Joshi, Sonal ; Kataria, Saurabh ; Shao, Yiwen ; Żelasko, Piotr ; Villalba, Jesús ; Khudanpur, Sanjeev ; Dehak, Najim ( January 2022 , Proc. Interspeech 2022)
5. One Man’s Trash is Another Man’s Treasure: Resisting Adversarial Examples by Adversarial Examples

   Xiao, Chang ; Zheng, Changxi ( June 2020 , IEEE Conference on Computer Vision and Pattern Recognition)

   Modern image classification systems are often built on deep neural networks, which suffer from adversarial examples—images with deliberately crafted, imperceptible noise to mislead the network’s classification. To defend against adversarial examples, a plausible idea is to obfuscate the network’s gradient with respect to the input image. This general idea has inspired a long line of defense methods. Yet, almost all of them have proven vulnerable. We revisit this seemingly flawed idea from a radically different perspective. We embrace the omnipresence of adversarial examples and the numerical procedure of crafting them, and turn this harmful attacking process into a useful defense mechanism. Our defense method is conceptually simple: before feeding an input image for classification, transform it by finding an adversarial example on a pre- trained external model. We evaluate our method against a wide range of possible attacks. On both CIFAR-10 and Tiny ImageNet datasets, our method is significantly more robust than state-of-the-art methods. Particularly, in comparison to adversarial training, our method offers lower training cost as well as stronger robustness. 

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