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Adversarial Attacks and Defenses

SIGKDD Explorations: Newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining, 2021
Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. Attackers add carefully-crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions.
Ninghao Liu, Mengnan Du, Ruocheng Guo
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Adversarial Attacks on Genotype Sequences

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022
ABSTRACT Adversarial attacks can drastically change the output of a method by performing a small change on its input. While they can be a useful framework to analyze worst-case robustness, they can also be used by malicious agents to perform damage in machine learning-based applications.
Daniel Mas Montserrat, Alexander G. Ioannidis   +1 more
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Adversarial Attack on Video Retrieval

2020 The 4th International Conference on Video and Image Processing, 2020
Recently adversarial examples have been reported to reveal the fragility of deep learning models. However, most adversarial attacks focus on classification task and less attention has been paid to retrieval task. In this paper, we are the first to investigate adversarial examples on the video retrieval system in both non-targeted and targeted attack ...
Ying Zou 0008, Chenglong Zhao, Bingbing Ni   +2 more
openaire   +1 more source

Adversarial Attack? Don't Panic

2018 4th International Conference on Big Data Computing and Communications (BIGCOM), 2018
Deep learning is playing a more and more important role in our daily life and scientific research such as autonomous systems, intelligent life and data mining. However, numerous studies have showed that deep learning with superior performance on many tasks may suffer from subtle perturbations constructed by attacker purposely, called adversarial ...
Feixia Min, Xiaofeng Qiu, Fan Wu
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Generative Transferable Adversarial Attack

Proceedings of the 3rd International Conference on Video and Image Processing, 2019
Despite their superior performance in computer vision tasks, deep neural networks are found to be vulnerable to adversarial examples, slightly perturbed examples that can mislead trained models. Moreover, adversarial examples are often transferable, i.e., adversaries crafted for one model can attack another model.
Yifeng Li, Ya Zhang 0002, Rui Zhang, Yanfeng Wang 0001   +3 more
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Componentwise Adversarial Attacks

2023
Lucas Beerens, Desmond J. Higham
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Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

ACM Computing Surveys, 2022
Ishai Rosenberg, Asaf Shabtai, Yuval Elovici   +2 more
exaly  

Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey

IEEE Communications Surveys and Tutorials, 2023
Yulong Wang, Shenghong Li, Xin Yuan
exaly  

Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity

ACM Computing Surveys, 2023
Shuai Zhou, Chi Liu, Dayong Ye
exaly  

A novel method for improving the robustness of deep learning-based malware detectors against adversarial attacks

Engineering Applications of Artificial Intelligence, 2022
Kamran Shaukat, Suhuai Luo, Vijay Varadharajan   +2 more
exaly