Results 51 to 60 of about 25,829 (292)
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT
, 2021 As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought.
William J. Buchanan +13 more
core +1 more source
Are facial attributes adversarially robust? [PDF]
2016 23rd International Conference on Pattern Recognition (ICPR), 2016 Facial attributes are emerging soft biometrics that have the potential to reject non-matches, for example, based on mismatching gender. To be usable in stand-alone systems, facial attributes must be extracted from images automatically and reliably. In this paper, we propose a simple yet effective solution for automatic facial attribute extraction by ...
Andras Rozsa +3 more
openaire +2 more sources
On the Adversarial Robustness of Multivariate Robust Estimation
CoRR, 2019 In this paper, we investigate the adversarial robustness of multivariate $M$-Estimators. In the considered model, after observing the whole dataset, an adversary can modify all data points with the goal of maximizing inference errors. We use adversarial influence function (AIF) to measure the asymptotic rate at which the adversary can change the ...
Erhan Bayraktar, Lifeng Lai
openaire +2 more sources
A Frequency Perspective of Adversarial Robustness
CoRR, 2021 Adversarial examples pose a unique challenge for deep learning systems. Despite recent advances in both attacks and defenses, there is still a lack of clarity and consensus in the community about the true nature and underlying properties of adversarial examples.
Shishira R. Maiya +5 more
openaire +2 more sources
Wasserstein Adversarial Robustness [PDF]
, 2020 Deep models, while being extremely flexible and accurate, are surprisingly vulnerable to ``small, imperceptible'' perturbations known as adversarial attacks.
Wu, Kaiwen
core
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective
, 2022 Neural networks have been widely applied in security applications such as spam and phishing detection, intrusion prevention, and malware detection. This black-box method, however, often has uncertainty and poor explainability in applications. Furthermore,
Teo, SG +6 more
core +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
Consistency Regularization for Adversarial Robustness
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Adversarial training (AT) is currently one of the most successful methods to obtain the adversarial robustness of deep neural networks. However, the phenomenon of robust overfitting, i.e., the robustness starts to decrease significantly during AT, has been problematic, not only making practitioners consider a bag of tricks for a successful training, e ...
Jihoon Tack +5 more
openaire +2 more sources
On the Interplay of Convolutional Padding and Adversarial Robustness
, 2023 It is common practice to apply padding prior to convolution operations to preserve the resolution of feature-maps in Convolutional Neural Networks (CNN). While many alternatives exist, this is often achieved by adding a border of zeros around the inputs.
Gavrikov, Paul, Keuper, Janis
core +1 more source
Adversarial Robustness in High-Dimensional Deep Learning [PDF]
, 2021 © 2021 Gregory Jeremiah KaranikasAs applications of deep learning continue to be discovered and implemented, the problem of robustness becomes increasingly important.
Karanikas, Gregory Jeremiah
core