Results 41 to 50 of about 79,418 (169)
Towards Adversarial Robustness for Multi-Mode Data through Metric Learning
Sensors, 2023 Adversarial attacks have become one of the most serious security issues in widely used deep neural networks. Even though real-world datasets usually have large intra-variations or multiple modes, most adversarial defense methods, such as adversarial ...
Sarwar Khan +3 more
doaj +1 more source
, 2018 It has been shown that adversaries can craft example inputs to neural networks which are similar to legitimate inputs but have been created to purposely cause the neural network to misclassify the input.
Athalye Anish +18 more
core +1 more source
Adversarial Attack and Defense on Deep Neural Network-Based Voice Processing Systems: An Overview
Applied Sciences, 2021 Voice Processing Systems (VPSes), now widely deployed, have become deeply involved in people’s daily lives, helping drive the car, unlock the smartphone, make online purchases, etc.
Xiaojiao Chen, Sheng Li, Hao Huang
doaj +1 more source
Adversarial Ranking Attack and Defense [PDF]
, 2020 Deep Neural Network (DNN) classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based image ranking systems remains under-explored. In this paper, we propose two attacks against deep ranking systems, i.e., Candidate Attack and Query Attack, that can ...
Zhou, Mo +4 more
openaire +2 more sources
Survey on adversarial attacks and defenses for object detection
Tongxin xuebao, 2023 In response to recent developments in adversarial attacks and defenses for object detection, relevant terms and concepts associated with object detection and adversarial learning were first introduced.Subsequently, according to the evolution process of ...
Xinxin WANG +6 more
doaj +2 more sources
A Mask-Based Adversarial Defense Scheme
Algorithms, 2022 Adversarial attacks hamper the functionality and accuracy of deep neural networks (DNNs) by meddling with subtle perturbations to their inputs. In this work, we propose a new mask-based adversarial defense scheme (MAD) for DNNs to mitigate the negative ...
Weizhen Xu +3 more
doaj +1 more source
GanDef: A GAN based Adversarial Training Defense for Neural Network Classifier
, 2019 Machine learning models, especially neural network (NN) classifiers, are widely used in many applications including natural language processing, computer vision and cybersecurity.
Khalil, Issa +2 more
core +3 more sources
You Can’t Fool All the Models: Detect Adversarial Samples via Pruning Models
IEEE Access, 2021 Many adversarial attack methods have investigated the security issue of deep learning models. Previous works on detecting adversarial samples show superior in accuracy but consume too much memory and computing resources.
Renxuan Wang +3 more
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Defense-VAE: A Fast and Accurate Defense Against Adversarial Attacks [PDF]
, 2020 Deep neural networks (DNNs) have been enormously successful across a variety of prediction tasks. However, recent research shows that DNNs are particularly vulnerable to adversarial attacks, which poses a serious threat to their applications in security-sensitive systems.
Li, Xiang, Ji, Shihao
openaire +2 more sources
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
, 2019 Deep neural networks (DNN) have been shown to be useful in a wide range of applications. However, they are also known to be vulnerable to adversarial samples.
Dong, Guoliang +4 more
core +1 more source