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IEEE AccessDeep learning (DL) has demonstrated remarkable achievements in various fields. Nevertheless, DL models encounter significant challenges in detecting and defending against adversarial samples (AEs).
Mohammed Nasser Al-Andoli +4 more
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Simple Transparent Adversarial Examples
CoRR, 2021 There has been a rise in the use of Machine Learning as a Service (MLaaS) Vision APIs as they offer multiple services including pre-built models and algorithms, which otherwise take a huge amount of resources if built from scratch. As these APIs get deployed for high-stakes applications, it's very important that they are robust to different ...
Jaydeep Borkar, Pin-Yu Chen
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HotFlip: White-Box Adversarial Examples for Text Classification
Annual Meeting of the Association for Computational Linguistics, 2017 We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier. We find that only a few manipulations are needed to greatly decrease the accuracy.
J. Ebrahimi +3 more
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IEEE AccessAn adversarial example (AE) is an attack method targeting machine learning, which is crafted by adding an imperceptible perturbation to input data to induce misclassification.
Hiroaki Maeshima, Akira Otsuka
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A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity
IEEE Access, 2020 Machine learning algorithms are widely utilized in cybersecurity. However, recent studies show that machine learning algorithms are vulnerable to adversarial examples.
Sicong Zhang, Xiaoyao Xie, Yang Xu
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Improving Adversarial Robustness of CNNs via Maximum Margin
Applied Sciences, 2022 In recent years, adversarial examples have aroused widespread research interest and raised concerns about the safety of CNNs. We study adversarial machine learning inspired by a support vector machine (SVM), where the decision boundary with maximum ...
Jiaping Wu, Zhaoqiang Xia, Xiaoyi Feng
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Adversarial Examples Are Not Real Features
Advances in Neural Information Processing Systems 36, 2023 The existence of adversarial examples has been a mystery for years and attracted much interest. A well-known theory by \citet{ilyas2019adversarial} explains adversarial vulnerability from a data perspective by showing that one can extract non-robust features from adversarial examples and these features alone are useful for classification.
Ang Li +3 more
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Maxwell’s Demon in MLP-Mixer: towards transferable adversarial attacks
CybersecurityModels based on MLP-Mixer architecture are becoming popular, but they still suffer from adversarial examples. Although it has been shown that MLP-Mixer is more robust to adversarial attacks compared to convolutional neural networks (CNNs), there has been
Haoran Lyu +5 more
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On the (Un-)Avoidability of Adversarial Examples
CoRR, 2021 The phenomenon of adversarial examples in deep learning models has caused substantial concern over their reliability. While many deep neural networks have shown impressive performance in terms of predictive accuracy, it has been shown that in many instances an imperceptible perturbation can falsely flip the network's prediction.
Sadia Chowdhury, Ruth Urner
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Ensemble Adversarial Example Defense Based on Generative Adversarial Network
工程科学与技术, 2022 Given the bottlenecks of existing adversarial example defense schemes, such as insufficient defense capability and high time consumption, an ensemble adversarial example defense scheme based on the generative adversarial network was proposed in this ...
Tianjie CAO +5 more
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