Results 11 to 20 of about 16,674 (259)
Experiments on Adversarial Examples for Deep Learning Model Using Multimodal Sensors [PDF]
Sensors, 2022 Recently, artificial intelligence (AI) based on IoT sensors has been widely used, which has increased the risk of attacks targeting AI. Adversarial examples are among the most serious types of attacks in which the attacker designs inputs that can cause ...
Ade Kurniawan +2 more
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Clustering Approach for Detecting Multiple Types of Adversarial Examples [PDF]
Sensors, 2022 With intentional feature perturbations to a deep learning model, the adversary generates an adversarial example to deceive the deep learning model.
Seok-Hwan Choi +3 more
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Defending against and generating adversarial examples together with generative adversarial networks [PDF]
Scientific ReportsAlthough deep neural networks have achieved great success in many tasks, they encounter security threats and are often fooled by adversarial examples, which are created by making slight modifications to pixel values. To address these problems, a novel DG-
Ying Wang, Xiao Liao, Wei Cui, Yang Yang
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Verifying the Causes of Adversarial Examples [PDF]
2020 25th International Conference on Pattern Recognition (ICPR), 2021 The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs, which mislead a classifier to incorrect outputs in high confidence. Limited by the extreme difficulty in examining a high-dimensional image space thoroughly, research on explaining and justifying the causes of adversarial ...
Li, H +4 more
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Efficient Adversarial Training With Transferable Adversarial Examples [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can require orders of magnitude additional training time due to high cost of generating strong adversarial examples during training.
Haizhong Zheng +4 more
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Semantic Adversarial Examples [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018 Deep neural networks are known to be vulnerable to adversarial examples, i.e., images that are maliciously perturbed to fool the model. Generating adversarial examples has been mostly limited to finding small perturbations that maximize the model prediction error.
Hossein Hosseini, Radha Poovendran
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Adversarial examples for models of code [PDF]
Proceedings of the ACM on Programming Languages, 2020 Neural models of code have shown impressive results when performing tasks such as predicting method names and identifying certain kinds of bugs. We show that these models are vulnerable to adversarial examples , and introduce a novel approach for attacking trained models of code using ...
Noam Yefet, Uri Alon 0002, Eran Yahav
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Adversarial Examples Generation Method Based on Image Color Random Transformation [PDF]
Jisuanji kexue, 2023 Although deep neural networks(DNNs) have good performance in most classification tasks,they are vulnerable to adversarial examples,making the security of DNNs questionable.Research designs to generate strongly aggressive adversarial examples can help ...
BAI Zhixu, WANG Hengjun, GUO Kexiang
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Evaluation of Model Quantization Method on Vitis-AI for Mitigating Adversarial Examples
IEEE Access, 2023 Adversarial examples (AEs) are typical model evasion attacks and security threats in deep neural networks (DNNs). One of the countermeasures is adversarial training (AT), and it trains DNNs by using a training dataset containing AEs to achieve robustness
Yuta Fukuda +2 more
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CoRR, 2020 Appears in: Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
Avishek Joey Bose +6 more
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