Results 71 to 80 of about 834,802 (152)

Adversarial defenses via vector quantization

Neurocomputing
This is the author-accepted version of our paper published in Neurocomputing.
Zhiyi Dong, Yongyi Mao
openaire   +2 more sources

Towards adversarial defense with multi-stage information fusion for visual tracking

Engineering Science and Technology, an International Journal
In recent years, the effectiveness and stealth of adversarial attack methods have been continuously improving, posing significant challenges to the robustness and accuracy of visual tracking.
Peng Gao, Wen-Jia Tang, Long Xu, Fei Wang, Ru-Yue Yuan   +4 more
doaj   +1 more source

DAD++: Improved Data-free Test Time Adversarial Defense [PDF]

, 2023
With the increasing deployment of deep neural networks in safety-critical applications such as self-driving cars, medical imaging, anomaly detection, etc., adversarial robustness has become a crucial concern in the reliability of these networks in real ...
Chakraborty, Anirban, Khatri, Inder, Randive, Shubham, Nayak, Gaurav Kumar, Rawal, Ruchit   +4 more
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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

"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
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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  

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

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 ...

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