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Adversarial Robustness

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.

Papers

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Showing 601–650 of 1746 papers

TitleStatusHype
A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles—0
Functional Network: A Novel Framework for Interpretability of Deep Neural Networks—0
A Survey of Neural Network Robustness Assessment in Image Recognition—0
Evaluating Adversarial Robustness with Expected Viable Performance—0
A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks—0
Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications—0
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions—0
Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging—0
Guided Interpolation for Adversarial Training—0
Framework for Progressive Knowledge Fusion in Large Language Models Through Structured Conceptual Redundancy Analysis—0
Evaluating Adversarial Robustness in the Spatial Frequency Domain—0
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification—0
Associative Adversarial Learning Based on Selective Attack—0
Evaluating Adversarial Robustness: A Comparison Of FGSM, Carlini-Wagner Attacks, And The Role of Distillation as Defense Mechanism—0
Non-adversarial Robustness of Deep Learning Methods for Computer Vision—0
Frequency Regularization for Improving Adversarial Robustness—0
Fixed Inter-Neuron Covariability Induces Adversarial Robustness—0
Erasing Concepts, Steering Generations: A Comprehensive Survey of Concept Suppression—0
Adversarial Robustness May Be at Odds With Simplicity—0
Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustness—0
A Spectral Perspective towards Understanding and Improving Adversarial Robustness—0
Ensemble Adversarial Defense via Integration of Multiple Dispersed Low Curvature Models—0
Improving Transformation-based Defenses against Adversarial Examples with First-order Perturbations—0
Adversarial examples attack based on random warm restart mechanism and improved Nesterov momentum—0
Flooding-X: Improving BERT's Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning—0
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks—0
Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense—0
Assessing Adversarial Robustness of Large Language Models: An Empirical Study—0
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification—0
Estimating the Adversarial Robustness of Attributions in Text with Transformers—0
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks—0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System—0
Enhancing Quantum Adversarial Robustness by Randomized Encodings—0
Evaluating adversarial robustness in simulated cerebellum—0
A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning—0
Adaptive Batch Normalization Networks for Adversarial Robustness—0
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations—0
Evaluating Adversarial Robustness on Document Image Classification—0
Adversarial Robustness is at Odds with Lazy Training—0
Finding a human-like classifier—0
Evaluating robustness of support vector machines with the Lagrangian dual approach—0
Evaluating the Adversarial Robustness for Fourier Neural Operators—0
Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review—0
Adversarial Examples are Misaligned in Diffusion Model Manifolds—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
Evaluating the Adversarial Robustness of Detection Transformers—0
Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN—0
Evaluating the Evaluators: Trust in Adversarial Robustness Tests—0
A Survey on Out-of-Distribution Evaluation of Neural NLP Models—0
Finding Dynamics Preserving Adversarial Winning Tickets—0
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All datasetsAdvGLUECIFAR-10ImageNetImageNet-AImageNet-CStylized ImageNetCIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeBERTa (single model)Accuracy0.61—Unverified
2ALBERT (single model)Accuracy0.59—Unverified
3T5 (single model)Accuracy0.57—Unverified
4SMART_RoBERTa (single model)Accuracy0.54—Unverified
5FreeLB (single model)Accuracy0.5—Unverified
6RoBERTa (single model)Accuracy0.5—Unverified
7InfoBERT (single model)Accuracy0.46—Unverified
8ELECTRA (single model)Accuracy0.42—Unverified
9BERT (single model)Accuracy0.34—Unverified
10SMART_BERT (single model)Accuracy0.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed classifierAccuracy95.23—Unverified
2Stochastic-LWTA/PGD/WideResNet-34-10Accuracy92.26—Unverified
3Stochastic-LWTA/PGD/WideResNet-34-5Accuracy91.88—Unverified
4GLOT-DRAccuracy84.13—Unverified
5TRADES-ANCRA/ResNet18Accuracy81.7—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (SGD, Cosine)Accuracy77.4—Unverified
2ResNet-50 (SGD, Step)Accuracy76.9—Unverified
3DeiT-S (AdamW, Cosine)Accuracy76.8—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy76.4—Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy12.2—Unverified
2ResNet-50 (SGD, Cosine)Accuracy3.3—Unverified
3ResNet-50 (SGD, Step)Accuracy3.2—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy3.1—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (AdamW, Cosine)mean Corruption Error (mCE)59.3—Unverified
2ResNet-50 (SGD, Step)mean Corruption Error (mCE)57.9—Unverified
3ResNet-50 (SGD, Cosine)mean Corruption Error (mCE)56.9—Unverified
4DeiT-S (AdamW, Cosine)mean Corruption Error (mCE)48—Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy13—Unverified
2ResNet-50 (SGD, Cosine)Accuracy8.4—Unverified
3ResNet-50 (SGD, Step)Accuracy8.3—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy8.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed ClassifierClean Accuracy85.21—Unverified
2ResNet18/MART-ANCRAClean Accuracy60.1—Unverified