Results 71 to 80 of about 852,288 (193)

"Defense Expenditures and Allied Cooperation" [PDF]

This paper investigates the implications of cooperative and non-cooperative defense spending of allied countries in conflicting blocs using static and leader-follower game models.
Toshihiro Ihori
core  

MAD: Meta Adversarial Defense Benchmark

, 2023
Adversarial training (AT) is a prominent technique employed by deep learning models to defend against adversarial attacks, and to some extent, enhance model robustness.
Peng, X., Zhou, D., Shi, J., Sun, G., Wu, L.   +4 more
core  

A knowledge distillation strategy for enhancing the adversarial robustness of lightweight automatic modulation classification models

IET Communications
Automatic modulation classification models based on deep learning models are at risk of being interfered by adversarial attacks. In an adversarial attack, the attacker causes the classification model to misclassify the received signal by adding carefully
Fanghao Xu, Chao Wang, Jiakai Liang, Chenyang Zuo, Keqiang Yue, Wenjun Li   +5 more
doaj   +1 more source

Adversarial Risk Análysis for Counterterrorism Modelling [PDF]

, 2013
Recent large scale terrorist attacks have raised interest in models for resource allocation against terrorist threats. The unifying theme in this area is the need to develop methods for the analysis of allocation decisions when risks stem from the ...
Ríos, Jesús, Ríos Insúa, David
core  

PuVAE: A Variational Autoencoder to Purify Adversarial Examples

IEEE Access, 2019
Deep neural networks are widely used and exhibit excellent performance in many areas. However, they are vulnerable to adversarial attacks that compromise networks at inference time by applying elaborately designed perturbations to input data.
Uiwon Hwang, Jaewoo Park, Hyemi Jang, Sungroh Yoon, Nam Ik Cho   +4 more
doaj   +1 more source

Moving Target Defense: Defending against Adversarial Defense

, 2019
: A defense-by-randomization framework is proposed as an effective defense mechanism against different types of adversarial attacks on neural networks. Experiments were conducted by selecting a combination of differently constructed image classification ...

core  

Adversarial Attacks and Defense using Energy-Based Image Models [PDF]

, 2022
In this article we briefly review current research in adversarial attacks and defenses and form a basis for a theoretical explanation as to why a generative energy model is the solution to the defense problem as it exists for securing naturally trained ...
Mitchell, Jonathan Craig
core   +1 more source

MAFFN-SAT: 3-D Point Cloud Defense via Multiview Adaptive Feature Fusion and Smooth Adversarial Training

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, Pang, S, Zhang, S, Du, A, Zhang, J   +4 more
core   +1 more source

Active Defense Against Voice Conversion Through Generative Adversarial Network [PDF]

Active defense is an important approach to counter speech deepfakes that threaten individuals’ privacy, property, and reputation. However, the existing works in this field suffer from issues such as time-consuming and ordinary defense effectiveness. This
Zhao, Guoying, Ma, Kaijie, Chen, Beijing, Dong, Shihang   +3 more
core   +1 more source

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, Yin, Jia-Li, , Lyhwa, Liu, Ximeng, Lin, Wei   +4 more
core   +1 more source