Results 21 to 30 of about 6,341 (246)
A Hybrid Adversarial Attack for Different Application Scenarios
Applied Sciences, 2020 Adversarial attack against natural language has been a hot topic in the field of artificial intelligence security in recent years. It is mainly to study the methods and implementation of generating adversarial examples. The purpose is to better deal with
Xiaohu Du +6 more
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Augmented Lagrangian Adversarial Attacks [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 ICCV 2021 (Poster).
Jérôme Rony +3 more
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Meta Gradient Adversarial Attack [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 In recent years, research on adversarial attacks has become a hot spot. Although current literature on the transfer-based adversarial attack has achieved promising results for improving the transferability to unseen black-box models, it still leaves a long way to go. Inspired by the idea of meta-learning, this paper proposes a novel architecture called
Zheng Yuan 0005 +5 more
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Adv-Eye: A Transfer-Based Natural Eye Makeup Attack on Face Recognition
IEEE Access, 2023 Deep face recognition models are vulnerable to adversarial samples generated by adversarial attack methods. However, current attack methods do not adequately represent the security problems of the deep FR models, because they either produce adversarial ...
Jiatian Pi +6 more
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Detection of Adversarial Attacks and Characterization of Adversarial Subspace [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 Adversarial attacks have always been a serious threat for any data-driven model. In this paper, we explore subspaces of adversarial examples in unitary vector domain, and we propose a novel detector for defending our models trained for environmental sound classification.
Mohammad Esmaeilpour +2 more
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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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Deflecting Adversarial Attacks
CoRR, 2020 There has been an ongoing cycle where stronger defenses against adversarial attacks are subsequently broken by a more advanced defense-aware attack. We present a new approach towards ending this cycle where we "deflect'' adversarial attacks by causing the attacker to produce an input that semantically resembles the attack's target class.
Yao Qin 0001 +4 more
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Applied Sciences, 2020 State-of-the-art neural network models are actively used in various fields, but it is well-known that they are vulnerable to adversarial example attacks.
Sanglee Park, Jungmin So
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Probabilistic Categorical Adversarial Attack & Adversarial Training
CoRR, 2022 The existence of adversarial examples brings huge concern for people to apply Deep Neural Networks (DNNs) in safety-critical tasks. However, how to generate adversarial examples with categorical data is an important problem but lack of extensive exploration.
Xu, Han +6 more
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