Results 31 to 40 of about 2,268,403 (339)
A Robust Adversarial Example Attack Based on Video Augmentation
Applied Sciences, 2023 Despite the success of learning-based systems, recent studies have highlighted video adversarial examples as a ubiquitous threat to state-of-the-art video classification systems.
Mingyong Yin +3 more
doaj +1 more source
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective [PDF]
IEEE Transactions on Dependable and Secure Computing, 2022 Neural networks have been widely applied in security applications such as spam and phishing detection, intrusion prevention, and malware detection. This black-box method, however, often has uncertainty and poor explainability in applications. Furthermore,
M. H. Meng +6 more
semanticscholar +1 more source
Adversarial Robustness under Long-Tailed Distribution [PDF]
Computer Vision and Pattern Recognition, 2021 Adversarial robustness has attracted extensive studies recently by revealing the vulnerability and intrinsic characteristics of deep networks. However, existing works on adversarial robustness mainly focus on balanced datasets, while real-world data ...
Tong Wu +4 more
semanticscholar +1 more source
Towards Adversarial Robustness via Feature Matching
IEEE Access, 2020 Image classification systems are known to be vulnerable to adversarial attacks, which are imperceptibly perturbed but lead to spectacularly disgraceful classification.
Zhuorong Li +4 more
doaj +1 more source
Study on Adversarial Robustness of Deep Learning Models Based on SVD [PDF]
Jisuanji kexue, 2023 The emergence of adversarial attacks poses a substantial threat to the large-scale deployment of deep neural networks(DNNs) in real-world scenarios,especially in security-related domains.Most of the current defense methods are based on heuristic ...
ZHAO Zitian, ZHAN Wenhan, DUAN Hancong, WU Yue
doaj +1 more source
Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack [PDF]
Computer Vision and Pattern Recognition, 2022 Defense models against adversarial attacks have grown significantly, but the lack of practical evaluation methods has hindered progress. Evaluation can be defined as looking for defense models' lower bound of robustness given a budget number of ...
Ye Liu +5 more
semanticscholar +1 more source
Are facial attributes adversarially robust? [PDF]
2016 23rd International Conference on Pattern Recognition (ICPR), 2016 Facial attributes are emerging soft biometrics that have the potential to reject non-matches, for example, based on mismatching gender. To be usable in stand-alone systems, facial attributes must be extracted from images automatically and reliably. In this paper, we propose a simple yet effective solution for automatic facial attribute extraction by ...
Rozsa, Andras +3 more
openaire +2 more sources
网络与信息安全学报, 2022 Adversarial training is one of the commonly used defense methods against adversarial attacks, by incorporating adversarial samples into the training process.However, the effectiveness of adversarial training heavily relied on the size of the trained ...
Bin WANG +6 more
doaj +3 more sources
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients [PDF]
AAAI Conference on Artificial Intelligence, 2017 Deep neural networks have proven remarkably effective at solving many classification problems, but have been criticized recently for two major weaknesses: the reasons behind their predictions are uninterpretable, and the predictions themselves can ...
A. Ross, F. Doshi-Velez
semanticscholar +1 more source
Adversarial Robustness Via Fisher-Rao Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Adversarial robustness has become a topic of growing interest in machine learning since it was observed that neural networks tend to be brittle. We propose an information-geometric formulation of adversarial defense and introduce FIRE, a new Fisher-Rao regularization for the categorical cross-entropy loss, which is based on the geodesic distance ...
Picot, Marine +6 more
openaire +3 more sources