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Feature autoencoder for detecting adversarial examples

https://doi.org/10.1002/int.22889
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/int.22315openalex-snapshot
Querying little is enough: Model inversion attack via latent information
inferredcites →https://doi.org/10.1002/int.22349openalex-snapshot
Camdar‐adv: Generating adversarial patches on 3D object
inferredcites →https://doi.org/10.1002/int.22529openalex-snapshot
Hybrid sequence‐based Android malware detection using natural language processing
inferredcites →https://doi.org/10.1002/int.22594openalex-snapshot
Opponent portrait for multiagent reinforcement learning in competitive environment
inferredcites →https://doi.org/10.1002/int.22621openalex-snapshot
HMM‐TCN‐based health assessment and state prediction for robot mechanical axis
inferredcites →https://doi.org/10.1002/int.22625openalex-snapshot
An empirical study of supervised email classification in Internet of Things: Practical performance and key influencing factors
inferredcites →https://doi.org/10.1002/int.22680openalex-snapshot
ELAA: An efficient local adversarial attack using model interpreters
inferredcites →https://doi.org/10.1007/s13042-020-01242-zopenalex-snapshot
Adversarial examples: attacks and defenses in the physical world
inferredcites →https://doi.org/10.1016/j.asoc.2020.106782openalex-snapshot
Secure video retrieval using image query on an untrusted cloud
inferredcites →https://doi.org/10.1016/j.ins.2020.09.064openalex-snapshot
PPCL: Privacy-preserving collaborative learning for mitigating indirect information leakage
inferredcites →https://doi.org/10.1016/j.ins.2021.01.046openalex-snapshot
MHAT: An efficient model-heterogenous aggregation training scheme for federated learning
inferredcites →https://doi.org/10.1016/j.isprsjprs.2020.01.025openalex-snapshot
Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images
inferredcites →https://doi.org/10.1016/j.jnca.2020.102632openalex-snapshot
Adversarial attacks on deep-learning-based SAR image target recognition
inferredcites →https://doi.org/10.1016/j.neucom.2020.08.083openalex-snapshot
Advanced variations of two-dimensional principal component analysis for face recognition
inferredcites →https://doi.org/10.1016/j.patcog.2018.07.023openalex-snapshot
Wild patterns: Ten years after the rise of adversarial machine learning
inferredcites →https://doi.org/10.1016/j.specom.2022.02.005openalex-snapshot
Unsupervised Automatic Speech Recognition: A review
inferredcites →https://doi.org/10.1109/cvpr.2016.282openalex-snapshot
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2017.17openalex-snapshot
Universal Adversarial Perturbations
inferredcites →https://doi.org/10.1109/cvpr.2019.00444openalex-snapshot
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
inferredcites →https://doi.org/10.1109/eurosp.2016.36openalex-snapshot
The Limitations of Deep Learning in Adversarial Settings
inferredcites →https://doi.org/10.1145/1143844.1143874openalex-snapshot
The relationship between Precision-Recall and ROC curves
inferredcites →https://doi.org/10.1145/3460120.3484757openalex-snapshot
DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks
inferredcites →https://doi.org/10.3837/tiis.2019.08.027openalex-snapshot
Adversarial Detection with Gaussian Process Regression-based Detector
inferredcites →https://doi.org/10.48550/arxiv.1911.05268openalex-snapshot
Adversarial Examples in Modern Machine Learning: A Review

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-031-78980-9_9openalex-snapshot
Purifying Adversarial Examples Using an Autoencoder
inferred← citeshttps://doi.org/10.1016/j.asoc.2026.115088openalex-snapshot
Enhancing adversarial example detection in DNNs: A joint CAE-TextCNN approach
inferred← citeshttps://doi.org/10.1109/access.2024.3354699openalex-snapshot
A Framework for Robust Deep Learning Models Against Adversarial Attacks Based on a Protection Layer Approach
inferred← citeshttps://doi.org/10.1109/access.2024.3520406openalex-snapshot
Adversarial Attack Detection Approach for Intrusion Detection Systems
inferred← citeshttps://doi.org/10.1109/cisp-bmei68103.2025.11259142openalex-snapshot
Seeing the Invisible: A Training-Free for Adversarial Example Detection via Adaptive Texture Enhancement and Frequency Analysis
inferred← citeshttps://doi.org/10.1109/icc52391.2025.11160917openalex-snapshot
Adaptive Continuous Adversarial Training (ACAT) to Enhance ML-NIDS Robustness
inferred← citeshttps://doi.org/10.1109/tdsc.2025.3548402openalex-snapshot
Adversarial Examples Detection With Enhanced Image Difference Features Based on Local Histogram Equalization
inferred← citeshttps://doi.org/10.1360/ssi-2025-0205openalex-snapshot
A detection method against adversarial example attack based on neural network decision boundary and consistency analysis
inferred← citeshttps://doi.org/10.3390/app132111880openalex-snapshot
Adversarial Example Detection and Restoration Defensive Framework for Signal Intelligent Recognition Networks
inferred← citeshttps://doi.org/10.3390/electronics14122382openalex-snapshot
One Possible Path Towards a More Robust Task of Traffic Sign Classification in Autonomous Vehicles Using Autoencoders
inferred← citeshttps://doi.org/10.4018/979-8-3693-1906-2.ch010openalex-snapshot
From Attack to Defense
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