Adversarial defense - Open Access .click

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Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process

IEEE Open Journal of the Industrial Electronics Society
Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of these technologies in industry is the vulnerability of neural networks to ...
Vitaliy Pozdnyakov +4 more
doaj +1 more source

Struggle with Adversarial Defense? Try Diffusion

CoRR
Adversarial attacks induce misclassification by introducing subtle perturbations. Recently, diffusion models are applied to the image classifiers to improve adversarial robustness through adversarial training or by purifying adversarial noise. However, diffusion-based adversarial training often encounters convergence challenges and high computational ...
Yujie Li +6 more
openaire +3 more sources

Symmetry Defense Against XGBoost Adversarial Perturbation Attacks

, 2023
We examine whether symmetry can be used to defend tree-based ensemble classifiers such as gradient-boosting decision trees (GBDTs) against adversarial perturbation attacks.
Lindqvist, Blerta
core

A Gradual Adversarial Training Method for Semantic Segmentation

Remote Sensing
Deep neural networks (DNNs) have achieved great success in various computer vision tasks. However, they are susceptible to artificially designed adversarial perturbations, which limit their deployment in security-critical applications.
Yinkai Zan, Pingping Lu, Tingyu Meng
doaj +1 more source

Advocating for Multiple Defense Strategies against Adversarial Examples

, 2020
International audienceIt has been empirically observed that defense mechanisms designed to protect neural networks against ∞ adversarial examples offer poor performance against 2 adversarial examples and vice versa. In this paper we conduct a geometrical
Araujo, Alexandre +3 more
core

Adversarial Robustness in Retinal Image Classification model : Gradient-Based Adversarial Attacks and defense mechanisms

Background: Convolutional neural networks (CNNs), have achieved great success in retinal image classification, aiding in the detection of diseases like diabetic retinopathy, cataract, and glaucoma.
Kota, Saideep
core +2 more sources

Traffic adversarial example attack and defense method based on explainable artificial intelligence

Tongxin xuebao
An adversarial example attack method based on XAI was proposed for AI-based NIDS. By identifying critical perturbation features with XAI and applying targeted perturbations while preserving traffic functionality, malicious traffic was gradually altered ...
MA Bowen +4 more
doaj

Research on adversarial attacks and defense performance of image classification models for automated driving systems

机车电传动
Image classification models have been widely applied to facilitate functions such as autonomous perception and positioning for automated driving in many transportation systems, including automobiles, autonomous rail and urban rail transit systems ...
TANG Jun, HUANG Wenjing, LI Shuang, WU Zili +3 more
doaj

Report of Secretary of Defense to the Congress on the ... Defense budget and ... Defense program.

Includes transition budgets, authorization request and defense programs.Title varies slightly.Report year ends June 30.Mode of access ...
United States. Dept. of Defense.
core

Randomized Purifier Based on Low Adversarial Transferability for Adversarial Defense

Deep neural networks are generally very vulnerable to adversarial attacks. In order to defend against adversarial attacks in classifiers, Adversarial Purification (AP) was developed to neutralize adversarial perturbations using a generative model at the ...
Byung Cheol Song, Sangjin Park, Yoojin Jung +2 more
core +1 more source

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deep learning

adversarial attacks
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image classification

image reconstruction
adversarial training
deep neural network