Results 81 to 90 of about 852,288 (193)

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

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

On the Defense of Spoofing Countermeasures Against Adversarial Attacks

, 2023
Advances in speech synthesis have exposed the vulnerability of spoofing countermeasure (CM) systems. Adversarial attacks exacerbate this problem, mainly due to the reliance of most CM models on deep neural networks.
Thien-Phuc Doan, Long Nguyen-Vu, Mai Bui, Souhwan Jung, Kihun Hong   +4 more
core   +1 more source

Game Theory for Adversarial Attacks and Defenses

CoRR, 2021
Adversarial attacks can generate adversarial inputs by applying small but intentionally worst-case perturbations to samples from the dataset, which leads to even state-of-the-art deep neural networks outputting incorrect answers with high confidence. Hence, some adversarial defense techniques are developed to improve the security and robustness of the ...
openaire   +2 more sources

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  

Defense Against Adversarial AI [PDF]

Journal of Cognitive Human-Computer Interaction
The increasing prevalence of deep learning technology has paved the way for a new era of AI-powered capabilities, promising revolutionary advancements across various societal domains such as healthcare and autonomous vehicles.
Bhavani G., Soundarya S., Tejashwini V., Sumitha S.   +3 more
doaj   +1 more source

Knowing is Half the Battle: Enhancing Clean Data Accuracy of Adversarial Robust Deep Neural Networks via Dual-Model Bounded Divergence Gating

IEEE Access
Significant advances have been made in recent years in improving the robustness of deep neural networks, particularly under adversarial machine learning scenarios where the data has been contaminated to fool networks into making undesirable predictions ...
Hossein Aboutalebi, Mohammad Javad Shafiee, Chi-En Amy Tai, Alexander Wong   +3 more
doaj   +1 more source

Adversarial Defense Method Based on Latent Representation Guidance for Remote Sensing Image Scene Classification. [PDF]

Entropy (Basel), 2023
Da Q, Zhang G, Wang W, Zhao Y, Lu D, Li S, Lang D.   +6 more
europepmc   +1 more source

Dictionary Learning Based Scheme for Adversarial Defense in Continuous-Variable Quantum Key Distribution. [PDF]

Entropy (Basel), 2023
Li S, Yin P, Zhou Z, Tang J, Huang D, Zhang L.   +5 more
europepmc   +1 more source

Adversarial training and deep k-nearest neighbors improves adversarial defense of glaucoma severity detection. [PDF]

Heliyon, 2022
Riza Rizky LM, Suyanto S.
europepmc   +1 more source