Results 41 to 50 of about 353,439 (278)
Adversarial Training for Free!
, 2019 Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks.
Davis, Larry S. +8 more
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Adversarially Regularising Neural [PDF]
Proceedings of the 22nd Conference on Computational Natural Language Learning, 2018 n ...
Minervini, Pasquale, Riedel, Sebastian
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Boundary Adversarial Examples Against Adversarial Overfitting
, 2022 Standard adversarial training approaches suffer from robust overfitting where the robust accuracy decreases when models are adversarially trained for too long. The origin of this problem is still unclear and conflicting explanations have been reported, i.e., memorization effects induced by large loss data or because of small loss data and growing ...
Hameed, Muhammad Zaid, Buesser, Beat
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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
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Adversarial Manifold Estimation
Foundations of Computational Mathematics, 2022 This paper studies the statistical query (SQ) complexity of estimating $d$-dimensional submanifolds in $\mathbb{R}^n$. We propose a purely geometric algorithm called Manifold Propagation, that reduces the problem to three natural geometric routines: projection, tangent space estimation, and point detection.
Aamari, Eddie, Knop, Alexander
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Adversarial Diversity and Hard Positive Generation
, 2016 State-of-the-art deep neural networks suffer from a fundamental problem - they misclassify adversarial examples formed by applying small perturbations to inputs.
Boult, Terrance E. +2 more
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Confrontation and the Criminal Defendant in a Hybrid Legal System: The Republic of North Macedonia
SEEU ReviewThis note analyzes the treatment of out-of-court statements in the Republic of North Macedonia’s (NMK) hybrid criminal procedure system, which blends adversarial and neoinquisitorial elements.
Siegel David M.
doaj +1 more source
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
Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks
, 2018 This work shows that it is possible to fool/attack recent state-of-the-art face detectors which are based on the single-stage networks. Successfully attacking face detectors could be a serious malware vulnerability when deploying a smart surveillance ...
D Chen +5 more
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Smooth adversarial examples [PDF]
EURASIP Journal on Information Security, 2020 AbstractThis paper investigates the visual quality of the adversarial examples. Recent papers propose to smooth the perturbations to get rid of high frequency artifacts. In this work, smoothing has a different meaning as it perceptually shapes the perturbation according to the visual content of the image to be attacked. The perturbation becomes locally
Zhang, Hanwei +3 more
openaire +4 more sources