Results 31 to 40 of about 4,795 (257)
Defense Against Universal Adversarial Perturbations [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Recent advances in Deep Learning show the existence of image-agnostic quasi-imperceptible perturbations that when applied to `any' image can fool a state-of-the-art network classifier to change its prediction about the image label. These `Universal Adversarial Perturbations' pose a serious threat to the success of Deep Learning in practice.
Naveed Akhtar, Jian Liu 0014, Ajmal Mian
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Certified Defenses for Adversarial Patches
CoRR, 2020 International Conference on Learning Representations, ICLR ...
Chiang, Ping-yeh +5 more
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Scaling provable adversarial defenses
CoRR, 2018 Recent work has developed methods for learning deep network classifiers that are provably robust to norm-bounded adversarial perturbation; however, these methods are currently only possible for relatively small feedforward networks. In this paper, in an effort to scale these approaches to substantially larger models, we extend previous work in three ...
Eric Wong 0001 +3 more
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The Defense of Adversarial Example with Conditional Generative Adversarial Networks [PDF]
Security and Communication Networks, 2020 Deep neural network approaches have made remarkable progress in many machine learning tasks. However, the latest research indicates that they are vulnerable to adversarial perturbations. An adversary can easily mislead the network models by adding well-designed perturbations to the input. The cause of the adversarial examples is unclear.
Fangchao Yu +3 more
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Text Adversarial Purification as Defense against Adversarial Attacks
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023 Accepted by ACL2023 main ...
Linyang Li, Demin Song, Xipeng Qiu
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Leveraging linear mapping for model-agnostic adversarial defense
Frontiers in Computer Science, 2023 In the ever-evolving landscape of deep learning, novel designs of neural network architectures have been thought to drive progress by enhancing embedded representations.
Huma Jamil +5 more
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Remote Sensing, 2023 Deep neural networks (DNNs) have been widely utilized in automatic visual navigation and recognition on modern unmanned aerial vehicles (UAVs), achieving state-of-the-art performances.
Zihao Lu, Hao Sun, Yanjie Xu
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Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
CoRR, 2020 Advances in the development of adversarial attacks have been fundamental to the progress of adversarial defense research. Efficient and effective attacks are crucial for reliable evaluation of defenses, and also for developing robust models. Adversarial attacks are often generated by maximizing standard losses such as the cross-entropy loss or maximum ...
Gaurang Sriramanan +3 more
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Multi-Line Defense Against Windows Adversarial Malware by Using Windows PE Information
IEEE AccessDeep learning has recently been in the spotlight among malware detection researchers in the sense that its training-based robust decision process can lead to efficient and effective malware detection.
Hannah Ho, Jun-Won Ho, Sungjin Ho
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Advanced Materials Technologies, EarlyView.The article overviews past and current efforts on caloric materials and systems, highlighting the contributions of Ames National Laboratory to the field. Solid‐state caloric heat pumping is an innovative method that can be implemented in a wide range of cooling and heating applications.
Agata Czernuszewicz +5 more
wiley +1 more source