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Defense Against Adversarial Attacks in Deep Learning

https://doi.org/10.3390/app9010076
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What this work says

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

inferredcites →https://doi.org/10.1038/nature12346openalex-snapshot
Connectomic reconstruction of the inner plexiform layer in the mouse retina
inferredcites →https://doi.org/10.1088/1742-6596/368/1/012030openalex-snapshot
Online particle detection with Neural Networks based on topological calorimetry information
inferredcites →https://doi.org/10.1109/cvpr.2017.17openalex-snapshot
Universal Adversarial Perturbations
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.2018.00357openalex-snapshot
Defense Against Universal Adversarial Perturbations
inferredcites →https://doi.org/10.1109/eurosp.2016.36openalex-snapshot
The Limitations of Deep Learning in Adversarial Settings
inferredcites →https://doi.org/10.1109/iccv.2017.153openalex-snapshot
Adversarial Examples for Semantic Segmentation and Object Detection
inferredcites →https://doi.org/10.1109/iccv.2017.56openalex-snapshot
SafetyNet: Detecting and Rejecting Adversarial Examples Robustly
inferredcites →https://doi.org/10.1109/iccv.2017.615openalex-snapshot
Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics
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/tnnls.2016.2550532openalex-snapshot
Adaptation to New Microphones Using Artificial Neural Networks With Trainable Activation Functions
inferredcites →https://doi.org/10.1126/science.1254806openalex-snapshot
The human splicing code reveals new insights into the genetic determinants of disease
inferredcites →https://doi.org/10.1162/neco_a_01143openalex-snapshot
Dense Associative Memory Is Robust to Adversarial Inputs
inferredcites →https://doi.org/10.1186/s40537-014-0007-7openalex-snapshot
Deep learning applications and challenges in big data analytics
inferredcites →https://doi.org/10.1609/aaai.v32i1.11504openalex-snapshot
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients
inferredcites →https://doi.org/10.3390/app8122416openalex-snapshot
Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation
inferredcites →https://doi.org/10.3390/electronics7100235openalex-snapshot
An Extreme Learning Machine Approach to Effective Energy Disaggregation

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-3-031-43567-6_1openalex-snapshot
Face Recognition Research and Development
inferred← citeshttps://doi.org/10.1007/978-3-031-43792-2_5openalex-snapshot
Attacks Against Machine Learning Systems: Analysis and GAN-based Approach to Protection
inferred← citeshttps://doi.org/10.1007/978-981-15-8086-4_59openalex-snapshot
Defense Against Adversarial Attack Using PCA
inferred← citeshttps://doi.org/10.1016/j.swevo.2022.101059openalex-snapshot
Brain programming is immune to adversarial attacks: Towards accurate and robust image classification using symbolic learning
inferred← citeshttps://doi.org/10.1109/cvprw67362.2025.00304openalex-snapshot
Rethinking the Role of Spatial Mixing
inferred← citeshttps://doi.org/10.1109/isdfs65363.2025.11012053openalex-snapshot
Adversarial Retraining and White-Box Attacks for Robust Malware Detection
inferred← citeshttps://doi.org/10.1109/iwcit67595.2025.11073010openalex-snapshot
Enhancing Adversarial Robustness With Energy-Based Models: a Novel Training Approach for Input Purification
inferred← citeshttps://doi.org/10.1109/smap63474.2024.00026openalex-snapshot
Adversarial Vulnerabilities and Defenses in Social Emotion Recognition
inferred← citeshttps://doi.org/10.1134/s1054661824700408openalex-snapshot
Approach to Detecting Attacks against Machine Learning Systems with a Generative Adversarial Network
inferred← citeshttps://doi.org/10.1145/3449726.3463179openalex-snapshot
Automated design of accurate and robust image classifiers with brain programming
inferred← citeshttps://doi.org/10.1186/s13638-020-01775-5openalex-snapshot
Defense against adversarial attacks in traffic sign images identification based on 5G
inferred← citeshttps://doi.org/10.15622/ia.24.6.8openalex-snapshot
Analysis of Modern Research on Protection against Adversarial Attacks in Energy Systems
inferred← citeshttps://doi.org/10.1615/annualrevheattransfer.2022042131openalex-snapshot
DEEP LEARNING IN HEAT TRANSFER
inferred← citeshttps://doi.org/10.3390/app11115235openalex-snapshot
Methods for Preventing Visual Attacks in Convolutional Neural Networks Based on Data Discard and Dimensionality Reduction
inferred← citeshttps://doi.org/10.3390/app11156826openalex-snapshot
Improved Surprise Adequacy Tools for Corner Case Data Description and Detection
inferred← citeshttps://doi.org/10.3390/bdcc3030043openalex-snapshot
RazorNet: Adversarial Training and Noise Training on a Deep Neural Network Fooled by a Shallow Neural Network
inferred← citeshttps://doi.org/10.3390/s22062194openalex-snapshot
Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions
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