Results 91 to 100 of about 5,389,393 (319)
, 2023 Machine learning is key for automated detection of malicious network activity to ensure that computer networks and organizations are protected against cyber security attacks.
Panagiotis Andriotis +8 more
core +1 more source
All‐Optical Reconfigurable Physical Unclonable Function for Sustainable Security
Advanced Materials, EarlyView.An all‐optical reconfigurable physical unclonable function (PUF) is demonstrated using plasmonic coupling–induced sintering of optically trapped gold nanoparticles, where Brownian motion serves as a robust entropy source. The resulting optical PUF exhibits high encoding density, strong resistance to modeling attacks, and practical authentication ...
Jang‐Kyun Kwak +4 more
wiley +1 more source
Defeating deep learning based de-anonymization attacks with adversarial example [PDF]
, 2023 Deep learning (DL) technologies bring new threats to network security. Website fingerprinting attacks (WFA) using DL models can distinguish victim’s browsing activities protected by anonymity technologies.
Guo, Zhongwen +5 more
core +1 more source
Inverse Design of Amorphous Materials With Targeted Properties
Advanced Materials, EarlyView.AMDEN is a diffusion model framework for the inverse design of amorphous materials with targeted properties. By incorporating Hamiltonian Monte Carlo refinement into the denoising process, the framework overcomes the challenge of generating thermally relaxed disordered structures.
Jonas A. Finkler +4 more
wiley +1 more source
On the (Un-)Avoidability of Adversarial Examples
CoRR, 2021 The phenomenon of adversarial examples in deep learning models has caused substantial concern over their reliability. While many deep neural networks have shown impressive performance in terms of predictive accuracy, it has been shown that in many instances an imperceptible perturbation can falsely flip the network's prediction.
Sadia Chowdhury, Ruth Urner
openaire +2 more sources
Advanced Materials, EarlyView.This review summarizes the principles and challenges of nonaqueous lithium‐oxygen batteries and recent advances in cathode catalysts, including carbon‐based materials, metals, oxides, sulfides, nitrides, carbides, and redox mediators. It highlights emerging design strategies and artificial intelligence‐driven approaches, emphasizing data‐assisted ...
Yuqing Yao +8 more
wiley +1 more source
CybersecuritySpeech recognition technology has brought revolutionary changes to our lives, but existing work has demonstrated the feasibility of using adversarial examples (AEs) to mislead speech recognition systems. Most existing adversarial attacks are designed for
Ruiyuan Li
doaj +1 more source
DualFlow: Generating imperceptible adversarial examples by flow field and normalize flow-based model
Frontiers in Neurorobotics, 2023 Recent adversarial attack research reveals the vulnerability of learning-based deep learning models (DNN) against well-designed perturbations. However, most existing attack methods have inherent limitations in image quality as they rely on a relatively ...
Renyang Liu +10 more
doaj +1 more source
Contrastive Learning with Adversarial Examples
CoRR, 2020 NeurIPS ...
Chih-Hui Ho, Nuno Vasconcelos
openaire +3 more sources
Transferability Ranking of Adversarial Examples
CoRR, 2022 Adversarial transferability in black-box scenarios presents a unique challenge: while attackers can employ surrogate models to craft adversarial examples, they lack assurance on whether these examples will successfully compromise the target model. Until now, the prevalent method to ascertain success has been trial and error-testing crafted samples ...
Levy, Mosh +3 more
openaire +2 more sources