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Adversarial Attack and Defense: A Survey

https://doi.org/10.3390/electronics11081283

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What this work says

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

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Towards Robust Neural Networks via Random Self-ensemble

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DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks

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Image Captioning with Semantic Attention

inferredcites →https://doi.org/10.1109/cvpr.2017.17openalex-snapshot

Universal Adversarial Perturbations

inferredcites →https://doi.org/10.1109/cvpr.2017.345openalex-snapshot

Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning

inferredcites →https://doi.org/10.1109/cvpr.2018.00191openalex-snapshot

Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser

inferredcites →https://doi.org/10.1109/cvpr.2018.00465openalex-snapshot

Generative Adversarial Perturbations

inferredcites →https://doi.org/10.1109/cvpr.2018.00894openalex-snapshot

Deflecting Adversarial Attacks with Pixel Deflection

inferredcites →https://doi.org/10.1109/cvpr.2019.00624openalex-snapshot

ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00031openalex-snapshot

DaST: Data-Free Substitute Training for Adversarial Attacks

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00110openalex-snapshot

Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01446openalex-snapshot

Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors

inferredcites →https://doi.org/10.1109/eurosp.2016.36openalex-snapshot

The Limitations of Deep Learning in Adversarial Settings

inferredcites →https://doi.org/10.1109/icassp.2012.6288864openalex-snapshot

Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition

inferredcites →https://doi.org/10.1109/icassp.2019.8683008openalex-snapshot

Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks

inferredcites →https://doi.org/10.1109/icassp.2019.8683044openalex-snapshot

APE-GAN: Adversarial Perturbation Elimination with GAN

inferredcites →https://doi.org/10.1109/iccv48922.2021.00759openalex-snapshot

Adversarial Example Detection Using Latent Neighborhood Graph

inferredcites →https://doi.org/10.1109/ijcnn.2019.8851823openalex-snapshot

AuxBlocks: Defense Adversarial Examples via Auxiliary Blocks

inferredcites →https://doi.org/10.1109/sp.2016.41openalex-snapshot

Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks

inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot

Towards Evaluating the Robustness of Neural Networks

inferredcites →https://doi.org/10.1109/tevc.2019.2890858openalex-snapshot

One Pixel Attack for Fooling Deep Neural Networks

inferredcites →https://doi.org/10.1109/wacv45572.2020.9093310openalex-snapshot

Adversarial Defense based on Structure-to-Signal Autoencoders

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Watch What You Just Said

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ZOO

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MagNet

inferredcites →https://doi.org/10.1145/3240765.3264699openalex-snapshot

Defensive dropout for hardening deep neural networks under adversarial attacks

inferredcites →https://doi.org/10.1609/aaai.v32i1.11672openalex-snapshot

Learning to Attack: Adversarial Transformation Networks

inferredcites →https://doi.org/10.1609/aaai.v34i04.5940openalex-snapshot

Structured Output Learning with Conditional Generative Flows

inferredcites →https://doi.org/10.18653/v1/e17-1066openalex-snapshot

Task-Specific Attentive Pooling of Phrase Alignments Contributes to Sentence Matching

inferredcites →https://doi.org/10.21437/interspeech.2016-326openalex-snapshot

Recurrent Models for Auditory Attention in Multi-Microphone Distant Speech Recognition

inferredcites →https://doi.org/10.21437/interspeech.2020-2059openalex-snapshot

ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

inferredcites →https://doi.org/10.24963/ijcai.2018/543openalex-snapshot

Generating Adversarial Examples with Adversarial Networks

inferredcites →https://doi.org/10.24963/ijcai.2019/833openalex-snapshot

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses

inferredcites →https://doi.org/10.3390/electronics9111817openalex-snapshot

Malicious PowerShell Detection Using Attention against Adversarial Attacks

inferredcites →https://doi.org/10.48550/arxiv.1706.06083openalex-snapshot

Towards Deep Learning Models Resistant to Adversarial Attacks

inferredcites →https://doi.org/10.48550/arxiv.1802.00420openalex-snapshot

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

inferredcites →https://doi.org/10.48550/arxiv.1805.06605openalex-snapshot

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

What points at this work

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

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Self-Adaptive Revisiting Awareness (Sara) Strategy: a Self-Adaptive Augmentation Technique for Enhancing Adversarial Robustness and Generalization Through Concentrated Focus on Modified Uncertain Samples

inferred← citeshttps://doi.org/10.1007/978-3-031-45316-8_34openalex-snapshot

Adversarial Attack Versus a Bio-Inspired Defensive Method for Image Classification

inferred← citeshttps://doi.org/10.1007/978-3-031-70536-6_10openalex-snapshot

Test Time Augmentation as a Defense Against Adversarial Attacks on Online Handwriting

inferred← citeshttps://doi.org/10.1007/978-3-031-74633-8_40openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-75778-5_3openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-78341-8_2openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-85386-9_3openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-032-08330-2_7openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-032-37261-1_24openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-981-95-0351-3_4openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-981-95-6123-0_12openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-981-96-5527-4_115openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/s00521-026-11869-8openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/s10462-024-11014-8openalex-snapshot

Deep learning adversarial attacks and defenses in autonomous vehicles: a systematic literature review from a safety perspective

inferred← citeshttps://doi.org/10.1007/s10489-025-06957-6openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/s10994-023-06470-2openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/s11263-025-02375-wopenalex-snapshot

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inferred← citeshttps://doi.org/10.1007/s11263-025-02631-zopenalex-snapshot

Proactive Schemes: A Survey of Adversarial Attacks for Social Good

inferred← citeshttps://doi.org/10.1007/s11416-025-00558-0openalex-snapshot

Minimal adversarial attack on the $$L_0$$ norm

inferred← citeshttps://doi.org/10.1007/s40747-025-01805-zopenalex-snapshot

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inferred← citeshttps://doi.org/10.1007/s42979-025-04658-yopenalex-snapshot

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inferred← citeshttps://doi.org/10.1007/s42979-026-05257-1openalex-snapshot

A Comprehensive Review on Adversarial Attacks and Detection Techniques in Deep Learning Models for Image Analysis

inferred← citeshttps://doi.org/10.1007/s44196-023-00266-xopenalex-snapshot

Boosting Adversarial Training Using Robust Selective Data Augmentation

inferred← citeshttps://doi.org/10.1016/b978-0-323-96129-5.00004-4openalex-snapshot

Dependence of the results of adversarial attacks on medical image modality, attack type, and defense methods

inferred← citeshttps://doi.org/10.1016/b978-0-44-326572-3.00023-1openalex-snapshot

Secured edge intelligence in smart transportation CPS

inferred← citeshttps://doi.org/10.1016/j.array.2025.100546openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.comnet.2025.111261openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.eswa.2024.123761openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.inffus.2025.103033openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.ins.2026.123842openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.jnca.2024.104031openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.jss.2025.112365openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.neucom.2025.131231openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.neunet.2024.107046openalex-snapshot

Spectral adversarial attack on graph via node injection

inferred← citeshttps://doi.org/10.1016/j.neunet.2025.107801openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.neunet.2026.108764openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.procs.2025.04.371openalex-snapshot

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inferred← citeshttps://doi.org/10.1038/s41598-025-22869-4openalex-snapshot

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inferred← citeshttps://doi.org/10.1049/cmu2.70177openalex-snapshot

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inferred← citeshttps://doi.org/10.1057/s41284-024-00435-3openalex-snapshot

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inferred← citeshttps://doi.org/10.1088/2631-8695/add5b9openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/access.2023.3241360openalex-snapshot

Rearranging Pixels is a Powerful Black-Box Attack for RGB and Infrared Deep Learning Models

inferred← citeshttps://doi.org/10.1109/access.2023.3326410openalex-snapshot

A Survey on Attacks and Their Countermeasures in Deep Learning: Applications in Deep Neural Networks, Federated, Transfer, and Deep Reinforcement Learning

inferred← citeshttps://doi.org/10.1109/access.2023.3345000openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/access.2024.3355924openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/access.2024.3395118openalex-snapshot

How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses

inferred← citeshttps://doi.org/10.1109/access.2024.3436812openalex-snapshot

A Study on Adversarial Sample Resistance and Defense Mechanism for Multimodal Learning-Based Phishing Website Detection

inferred← citeshttps://doi.org/10.1109/access.2024.3439741openalex-snapshot

A Comprehensive Risk Analysis Method for Adversarial Attacks on Biometric Authentication Systems

inferred← citeshttps://doi.org/10.1109/access.2025.3567195openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/aipr57179.2022.10092228openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/apsec66846.2025.00029openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/atc63255.2024.10908263openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/ats64447.2024.10915409openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/ats64447.2024.10915428openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/comst.2023.3319492openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/cvprw63382.2024.00032openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/iaecst64597.2024.11117374openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/icairc64177.2024.10900089openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/icassp49357.2023.10097028openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/iccea65460.2025.11103113openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/iccvw60793.2023.00477openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/icesc60852.2024.10690061openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/iciscois62701.2026.11447822openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/icmi60790.2024.10586025openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/icort56052.2023.10249152openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/innocomp63224.2024.00059openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/iscas66217.2026.11562850openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/isgt59692.2024.10454163openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/isvlsi65124.2025.11130213openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/jstars.2024.3399780openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/mce.2024.3522524openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/ojies.2024.3401396openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/rivf68649.2025.11365242openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/tase.2025.3550305openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/tc.2023.3292001openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/tcasai.2026.3674059openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/tcomm.2026.3704240openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/tmc.2024.3396860openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/vtc2025-spring65109.2025.11174576openalex-snapshot

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inferred← citeshttps://doi.org/10.1145/3719292openalex-snapshot

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inferred← citeshttps://doi.org/10.1145/3789982.3790029openalex-snapshot

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inferred← citeshttps://doi.org/10.11834/jig.230423openalex-snapshot

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inferred← citeshttps://doi.org/10.1186/s42400-026-00600-8openalex-snapshot

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inferred← citeshttps://doi.org/10.15622/ia.24.6.8openalex-snapshot

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inferred← citeshttps://doi.org/10.21203/rs.3.rs-3518720/v1openalex-snapshot

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inferred← citeshttps://doi.org/10.2139/ssrn.5387355openalex-snapshot

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inferred← citeshttps://doi.org/10.21681/2311-2024-1-24-37openalex-snapshot

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inferred← citeshttps://doi.org/10.21681/2311-3456-2024-1-24-37openalex-snapshot

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inferred← citeshttps://doi.org/10.22144/ctujoisd.2023.030openalex-snapshot

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inferred← citeshttps://doi.org/10.2478/cait-2025-0028openalex-snapshot

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inferred← citeshttps://doi.org/10.32604/cmc.2024.058675openalex-snapshot

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inferred← citeshttps://doi.org/10.33099/2311-7249/2023-47-2-139-146openalex-snapshot

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inferred← citeshttps://doi.org/10.33166/aetic.2025.04.005openalex-snapshot

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inferred← citeshttps://doi.org/10.3390/app13179698openalex-snapshot

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inferred← citeshttps://doi.org/10.3390/app131910972openalex-snapshot

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inferred← citeshttps://doi.org/10.3390/app132011313openalex-snapshot

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inferred← citeshttps://doi.org/10.3390/app14188119openalex-snapshot

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inferred← citeshttps://doi.org/10.3390/app15137533openalex-snapshot

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inferred← citeshttps://doi.org/10.3390/app15168859openalex-snapshot

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inferred← citeshttps://doi.org/10.3390/electronics14020374openalex-snapshot

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inferred← citeshttps://doi.org/10.3390/jcp6040132openalex-snapshot

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inferred← citeshttps://doi.org/10.3390/jimaging11030073openalex-snapshot

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inferred← citeshttps://doi.org/10.4018/979-8-3373-2200-1.ch004openalex-snapshot

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inferred← citeshttps://doi.org/10.52591/lxai2023061811openalex-snapshot

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inferred← citeshttps://doi.org/10.5604/01.3001.0055.3255openalex-snapshot

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inferred← citeshttps://doi.org/10.5753/sbsc_estendido.2026.20333openalex-snapshot

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inferred← citeshttps://doi.org/10.5772/acrt.15openalex-snapshot

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inferred← citeshttps://doi.org/10.58496/adsa/2024/011openalex-snapshot

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