A Systematic Survey On Security in Anonymity Networks: Vulnerabilities, Attacks, Defenses, and Formalization
D Chao, D Xu, F Gao, C Zhang… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
The importance of safeguarding individuals' privacy rights in online activities is unmistakable
in today's anonymity networks. Since the introduction of Mixnet by Chaum, numerous …
in today's anonymity networks. Since the introduction of Mixnet by Chaum, numerous …
The double-edged sword of implicit bias: Generalization vs. robustness in relu networks
In this work, we study the implications of the implicit bias of gradient flow on generalization
and adversarial robustness in ReLU networks. We focus on a setting where the data …
and adversarial robustness in ReLU networks. We focus on a setting where the data …
The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression
H Hassani, A Javanmard - The Annals of Statistics, 2024 - projecteuclid.org
The curse of overparametrization in adversarial training: Precise analysis of robust
generalization for random features regressi Page 1 The Annals of Statistics 2024, Vol. 52, No. 2 …
generalization for random features regressi Page 1 The Annals of Statistics 2024, Vol. 52, No. 2 …
Learning provably robust estimators for inverse problems via jittering
A Krainovic, M Soltanolkotabi… - Advances in Neural …, 2024 - proceedings.neurips.cc
Deep neural networks provide excellent performance for inverse problems such as
denoising. However, neural networks can be sensitive to adversarial or worst-case …
denoising. However, neural networks can be sensitive to adversarial or worst-case …
Adversarial examples might be avoidable: The role of data concentration in adversarial robustness
The susceptibility of modern machine learning classifiers to adversarial examples has
motivated theoretical results suggesting that these might be unavoidable. However, these …
motivated theoretical results suggesting that these might be unavoidable. However, these …
Resilient Machine Learning: Advancement, Barriers, and Opportunities in the Nuclear Industry
The widespread adoption and success of Machine Learning (ML) technologies depend on
thorough testing of the resilience and robustness to adversarial attacks. The testing should …
thorough testing of the resilience and robustness to adversarial attacks. The testing should …
Adversarial examples are not real features
The existence of adversarial examples has been a mystery for years and attracted much
interest. A well-known theory by\citet {ilyas2019adversarial} explains adversarial …
interest. A well-known theory by\citet {ilyas2019adversarial} explains adversarial …
Adversarial training from mean field perspective
S Kumano, H Kera, T Yamasaki - Advances in Neural …, 2024 - proceedings.neurips.cc
Although adversarial training is known to be effective against adversarial examples, training
dynamics are not well understood. In this study, we present the first theoretical analysis of …
dynamics are not well understood. In this study, we present the first theoretical analysis of …
Reverse Engineering of Deceptions on Machine-and Human-Centric Attacks
This work presents a comprehensive exploration of Reverse Engineering of Deceptions
(RED) in the field of adversarial machine learning. It delves into the intricacies of machine …
(RED) in the field of adversarial machine learning. It delves into the intricacies of machine …
Adversarial ink: Componentwise backward error attacks on deep learning
Deep neural networks are capable of state-of-the-art performance in many classification
tasks. However, they are known to be vulnerable to adversarial attacks—small perturbations …
tasks. However, they are known to be vulnerable to adversarial attacks—small perturbations …