Results 31 to 40 of about 1,662,189 (295)
Hadamard’s Defense Against Adversarial Examples
IEEE Access, 2021 Adversarial images have become an increasing concern in real-world image recognition applications with deep neural networks (DNN). We observed that all the architectures in DNN use one-hot encoding after a softmax layer.
Angello Hoyos, Ubaldo Ruiz, Edgar Chavez
doaj +1 more source
Recovery of Adversarial Examples Based on SmsGAN [PDF]
Zhengzhou Daxue xuebao. Gongxue ban, 2021 Due to adversarial examples′ serious interference to the detection models based on deep learning, a recovery method of adversarial examples based on stochastic multihlter statistical generative adversarial network (SmsGAN) was proposed in this work.
ZHAO Junjie, WANG Jinwei
doaj +1 more source
Exploiting Doubly Adversarial Examples for Improving Adversarial Robustness
, 2022 Deep neural networks have shown outstanding performance in various areas, but adversarial examples can easily fool them. Although strong adversarial attacks have defeated diverse adversarial defense methods, adversarial training, which augments training ...
Cho, Seungju +3 more
core +1 more source
Adversarial examples in remote sensing [PDF]
Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2018 This paper considers attacks against machine learning algorithms used in remote sensing applications, a domain that presents a suite of challenges that are not fully addressed by current research focused on natural image data such as ImageNet. In particular, we present a new study of adversarial examples in the context of satellite image classification
Wojciech Czaja +4 more
openaire +3 more sources
CoRR, 2020 Appears in: Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
Avishek Joey Bose +6 more
openaire +4 more sources
Adversarial Examples for Generative Models [PDF]
2018 IEEE Security and Privacy Workshops (SPW), 2018 We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classification tasks.
Jernej Kos, Ian Fischer, Dawn Song
openaire +4 more sources
“Adversarial Examples” for Proof-of-Learning
2022 IEEE Symposium on Security and Privacy (SP), 2022 To appear in the 43rd IEEE Symposium on Security and ...
Rui Zhang 0118 +5 more
openaire +4 more sources
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
doaj +3 more sources
Instance attack: an explanation-based vulnerability analysis framework against DNNs for malware detection [PDF]
PeerJ Computer Science, 2023 Deep neural networks (DNNs) are increasingly being used in malware detection and their robustness has been widely discussed. Conventionally, the development of an adversarial example generation scheme for DNNs involves either detailed knowledge ...
Ruijin Sun +6 more
doaj +2 more sources
Adversarial Attack for SAR Target Recognition Based on UNet-Generative Adversarial Network
Remote Sensing, 2021 Some recent articles have revealed that synthetic aperture radar automatic target recognition (SAR-ATR) models based on deep learning are vulnerable to the attacks of adversarial examples and cause security problems.
Chuan Du, Lei Zhang
doaj +1 more source