Results 51 to 60 of about 5,384,791 (287)
An interpretable approach for trustworthy intrusion detection systems against evasion samples
CTU Journal of Innovation and Sustainable Development, 2023 In recent years, Deep Neural Networks (DNN) have demonstrated remarkable success in various domains, including Intrusion Detection Systems (IDS). The ability of DNN to learn complex patterns from large datasets has significantly improved IDS performance,
Ngoc Tai Nguyen +3 more
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Detecting Adversarial Examples in Batches -- a geometrical approach
CoRR, 2022 Many deep learning methods have successfully solved complex tasks in computer vision and speech recognition applications. Nonetheless, the robustness of these models has been found to be vulnerable to perturbed inputs or adversarial examples, which are imperceptible to the human eye, but lead the model to erroneous output decisions.
Danush Kumar Venkatesh, Peter Steinbach
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Detecting Adversarial Examples
CoRRDeep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples. While numerous successful adversarial attacks have been proposed, defenses against these attacks remain relatively understudied. Existing defense approaches either focus on negating the effects of perturbations caused by the attacks to restore the DNNs' original ...
Furkan Mumcu, Yasin Yilmaz 0001
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Adversarial Examples Are Not Easily Detected
Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, 2017 Neural networks are known to be vulnerable to adversarial examples: inputs that are close to natural inputs but classified incorrectly. In order to better understand the space of adversarial examples, we survey ten recent proposals that are designed for detection and compare their efficacy.
Nicholas Carlini, David A. Wagner 0001
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Towards Robust Detection of Adversarial Examples
, 2017 Although the recent progress is substantial, deep learning methods can be vulnerable to the maliciously generated adversarial examples. In this paper, we present a novel training procedure and a thresholding test strategy, towards robust detection of adversarial examples.
Tianyu Pang +3 more
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Adversarial Examples for Semantic Segmentation and Object Detection [PDF]
2017 IEEE International Conference on Computer Vision (ICCV), 2017 To appear in ICCV ...
Cihang Xie +5 more
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Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection [PDF]
, 2020 Social media has become very popular and important in people’s lives, as personal ideas, beliefs and opinions are expressed and shared through them. Unfortunately, social networks, and specifically Twitter, suffer from massive existence and perpetual ...
Nikolaos Pitropakis +7 more
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Adversarial Attacks to Manipulate Target Localization of Object Detector
IEEE AccessAdversarial attack has gradually become an important branch in the field of artificial intelligence security, where the potential threat brought by adversarial example attack is more not to be ignored.
Kai Xu +7 more
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Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network
IEEE Access, 2018 Deep neural networks (DNNs) are widely used for image recognition, speech recognition, pattern analysis, and intrusion detection. Recently, the adversarial example attack, in which the input data are only slightly modified, although not an issue for ...
Hyun Kwon +4 more
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Adversarial attacks and defenses in deep learning
网络与信息安全学报, 2020 The adversarial example is a modified image that is added imperceptible perturbations, which can make deep neural networks decide wrongly. The adversarial examples seriously threaten the availability of the system and bring great security risks to the ...
LIU Ximeng +2 more
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