Results 81 to 90 of about 834,802 (152)
Adversarial attacks pose a significant threat to deep neural networks (DNNs) used for 3-D point cloud classification, especially in safety-critical applications.
Gao, Y +4 more
core +1 more source
Stylized Pairing for Robust Adversarial Defense
, 2022 Recent studies show that deep neural networks (DNNs)-based object recognition algorithms overly rely on object textures rather than global object shapes, and DNNs are also vulnerable to human-less perceptible adversarial perturbations. Based on these two
Xiao Liu, Wentao Zhao, Dejian Guan
core +1 more source
On Adaptive Attacks to Adversarial Example Defenses
CoRR, 2020 Adaptive attacks have (rightfully) become the de facto standard for evaluating defenses to adversarial examples. We find, however, that typical adaptive evaluations are incomplete. We demonstrate that thirteen defenses recently published at ICLR, ICML and NeurIPS---and chosen for illustrative and pedagogical purposes---can be circumvented despite ...
Florian Tramèr +3 more
openaire +3 more sources
Adversarial-Inspired Backdoor Defense via Bridging Backdoor and Adversarial Attacks
Backdoor attacks and adversarial attacks are two major security threats to deep neural networks (DNNs), with the former one is a training-time data poisoning attack that aims to implant backdoor triggers into models by injecting trigger patterns into ...
Wang, Weijian +4 more
core +1 more source
On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense
, 2023 Under adversarial attacks, time series regression and classification are vulnerable. Adversarial defense, on the other hand, can make the models more resilient.
Bashyal, Bidhan, Galib, Asadullah Hill
core
Adversarial Domain Generalization Defense for Automatic Modulation Classification
Automatic modulation classification AMC technology plays a vital role in the sixth generation mobile system 6G However deep learning DL based AMC models possess unexpected vulnerability against adversarial examples which seriously affects their ...
Liu, J, Zhang, S, Lin, Y, Bao, Z, Yu, S
core +1 more source
Image super-resolution as a defense against adversarial attacks
, 2020 Convolutional Neural Networks have achieved significant success across multiple computer vision tasks. However, they are vulnerable to carefully crafted, human-imperceptible adversarial noise patterns which constrain their deployment in critical security-
Mustafa, Aamir +4 more
core +1 more source
Defensive Distillation is Not Robust to Adversarial Examples
CoRR, 2016 We show that defensive distillation is not secure: it is no more resistant to targeted misclassification attacks than unprotected neural networks.
Nicholas Carlini, David A. Wagner 0001
openaire +3 more sources
Symmetry Defense Against CNN Adversarial Perturbation Attacks [PDF]
, 2023 This paper uses symmetry to make Convolutional Neural Network classifiers (CNNs) robust against adversarial perturbation attacks. Such attacks add perturbation to original images to generate adversarial images that fool classifiers such as road sign ...
Lindqvist, Blerta
core
Moving target defense for adaptive adversaries
2013 IEEE International Conference on Intelligence and Security Informatics, 2013 Machine learning (ML) plays a central role in the solution of many security problems, for example enabling malicious and innocent activities to be rapidly and accurately distinguished and appropriate actions to be taken. Unfortunately, a standard assumption in ML - that the training and test data are identically distributed - is typically violated in ...
Richard Colbaugh, Kristin Glass
openaire +2 more sources