Results 51 to 60 of about 1,236,597 (154)
Transferable Adversarial Attack based on Integrated Gradients [PDF]
International Conference on Learning Representations, 2022 The vulnerability of deep neural networks to adversarial examples has drawn tremendous attention from the community. Three approaches, optimizing standard objective functions, exploiting attention maps, and smoothing decision surfaces, are commonly used ...
Y. Huang, A. Kong
semanticscholar +1 more source
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition [PDF]
Neural Information Processing Systems, 2022 Deep learning models have shown their vulnerability when dealing with adversarial attacks. Existing attacks almost perform on low-level instances, such as pixels and super-pixels, and rarely exploit semantic clues.
Shuai Jia +6 more
semanticscholar +1 more source
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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Plant Disease Classification and Adversarial Attack Using SimAM-EfficientNet and GP-MI-FGSM
Sustainability, 2023 Plant diseases have received common attention, and deep learning has also been applied to plant diseases. Deep neural networks (DNNs) have achieved outstanding results in plant diseases.
Hao-ling You, Yufang Lu, Haihua Tang
semanticscholar +1 more source
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
openaire +2 more sources
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
doaj +1 more source
Mathematics, 2023 Deep learning approaches have demonstrated great achievements in the field of computer-aided medical image analysis, improving the precision of diagnosis across a range of medical disorders.
G. W. Muoka +8 more
semanticscholar +1 more source
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios [PDF]
2022 IEEE Intelligent Vehicles Symposium (IV), 2022 Visual detection is a key task in autonomous driving, and it serves as a crucial foundation for self-driving planning and control. Deep neural networks have achieved promising results in various visual tasks, but they are known to be vulnerable to ...
Jung Im Choi, Qing Tian
semanticscholar +1 more source
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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