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Sparsity based defense against adversarial examples: v1.0
, 2020 Sparsity-based defense against adversarial attacks on machine learning classifiers. Contains code for the following papers: S. Gopalakrishnan, Z. Marzi, U. Madhow, R.
Soorya Gopalakrishnan
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ATWM: Defense against adversarial malware based on adversarial training [PDF]
, 2023 Deep learning technology has made great achievements in the field of image. In order to defend against malware attacks, researchers have proposed many Windows malware detection models based on deep learning.
Li, Kun, Guo, Wei, Zhang, Fan
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Big Data and Cognitive Computing, 2020 In this work, we propose a novel defense system against adversarial examples leveraging the unique power of Generative Adversarial Networks (GANs) to generate new adversarial examples for model retraining. To do so, we develop an automated pipeline using
Shayan Taheri +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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Robust Adversarial Defense by Tensor Factorization [PDF]
, 2023 As machine learning techniques become increasingly prevalent in data analysis, the threat of adversarial attacks has surged, necessitating robust defense mechanisms.
Alexandrov, Boian +5 more
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Defending Against Adversarial Attacks with Camera Image Pipelines
, 2023 Existing neural networks for computer vision tasks are vulnerable to adversarial attacks: adding imperceptible perturbations to the input images can fool these models into making a false prediction on an image that was correctly predicted without the ...
Zhang, Yuxuan
core
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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An Empirical Review of Adversarial Defenses
CoRR, 2020 19 pages, 8 Figures, Report Reviewed by Vivek ...
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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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