Results 61 to 70 of about 5,380,268 (331)
Adversarial Examples and Metrics
CoRR, 2020 25 pages, 1 figure, under submission, fixe typos from previous ...
Nico Döttling +3 more
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“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
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Simplicial-Map Neural Networks Robust to Adversarial Examples [PDF]
, 2021 Broadly speaking, an adversarial example against a classification model occurs when a small perturbation on an input data point produces a change on the output label assigned by the model.
Rocio Gonzalez-Diaz +11 more
core +1 more source
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
Unrestricted Adversarial Examples
CoRR, 2018 We introduce a two-player contest for evaluating the safety and robustness of machine learning systems, with a large prize pool. Unlike most prior work in ML robustness, which studies norm-constrained adversaries, we shift our focus to unconstrained adversaries.
Tom B. Brown +5 more
openaire +2 more sources
Adversarial Examples: Attacks and Defenses for Deep Learning [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2017 With rapid progress and significant successes in a wide spectrum of applications, deep learning is being applied in many safety-critical environments. However, deep neural networks (DNNs) have been recently found vulnerable to well-designed input samples
Xiaoyong Yuan +3 more
semanticscholar +1 more source
Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer [PDF]
ACM Multimedia, 2023 Deep neural networks are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on clean inputs. Although many attack methods can achieve high success rates in the white-box setting, they also exhibit weak ...
Zhijin Ge +6 more
semanticscholar +1 more source
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
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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
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 +2 more sources