SOTAVerified

Adversarial Robustness

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

Papers

Showing 15011550 of 1746 papers

TitleStatusHype
Efficient Exact Verification of Binarized Neural NetworksCode1
Measuring Adversarial Robustness using a Voronoi-Epsilon AdversaryCode0
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid DecoderCode1
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?0
Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks0
Improving Adversarial Robustness Requires Revisiting Misclassified ExamplesCode1
Bridging Mode Connectivity in Loss Landscapes and Adversarial RobustnessCode1
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks0
Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial RobustnessCode0
QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks0
How to compare adversarial robustness of classifiers from a global perspectiveCode0
Certifying Joint Adversarial Robustness for Model EnsemblesCode0
Adversarial Attack on Deep Learning-Based Splice LocalizationCode1
Adversarial Robustness Guarantees for Random Deep Neural NetworksCode0
Adversarially Robust Streaming Algorithms via Differential Privacy0
Adversarial Weight Perturbation Helps Robust GeneralizationCode1
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning0
Approximate Manifold Defense Against Multiple Adversarial PerturbationsCode0
SOAR: Second-Order Adversarial Regularization0
Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit PlanesCode1
Towards Deep Learning Models Resistant to Large PerturbationsCode0
Improving out-of-distribution generalization via multi-task self-supervised pretraining0
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-TuningCode1
Challenging the adversarial robustness of DNNs based on error-correcting output codes0
Defense Through Diverse Directions0
Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear ActivationsCode1
Architectural Resilience to Foreground-and-Background Adversarial NoiseCode0
Adversarial Robustness on In- and Out-Distribution Improves ExplainabilityCode1
SAT: Improving Adversarial Training via Curriculum-Based Loss Smoothing0
Toward Adversarial Robustness via Semi-supervised Robust TrainingCode1
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty DetectionCode1
Adversarial Machine Learning: Bayesian PerspectivesCode1
Adversarial Vertex Mixup: Toward Better Adversarially Robust GeneralizationCode1
Metrics and methods for robustness evaluation of neural networks with generative modelsCode0
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacksCode1
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial RobustnessCode1
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative ModelsCode0
Defense-PointNet: Protecting PointNet Against Adversarial Attacks0
Attacks Which Do Not Kill Training Make Adversarial Learning StrongerCode1
Learning Adversarially Robust Representations via Worst-Case Mutual Information MaximizationCode1
Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural NetworksCode0
Towards Certifiable Adversarial Sample Detection0
Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness0
Scalable Quantitative Verification For Deep Neural Networks0
Hold me tight! Influence of discriminative features on deep network boundariesCode1
CEB Improves Model RobustnessCode0
Adversarial Robustness for CodeCode1
Semialgebraic Optimization for Lipschitz Constants of ReLU NetworksCode0
Random Smoothing Might be Unable to Certify _ Robustness for High-Dimensional ImagesCode1
Renofeation: A Simple Transfer Learning Method for Improved Adversarial RobustnessCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeBERTa (single model)Accuracy0.61Unverified
2ALBERT (single model)Accuracy0.59Unverified
3T5 (single model)Accuracy0.57Unverified
4SMART_RoBERTa (single model)Accuracy0.54Unverified
5FreeLB (single model)Accuracy0.5Unverified
6RoBERTa (single model)Accuracy0.5Unverified
7InfoBERT (single model)Accuracy0.46Unverified
8ELECTRA (single model)Accuracy0.42Unverified
9BERT (single model)Accuracy0.34Unverified
10SMART_BERT (single model)Accuracy0.3Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed classifierAccuracy95.23Unverified
2Stochastic-LWTA/PGD/WideResNet-34-10Accuracy92.26Unverified
3Stochastic-LWTA/PGD/WideResNet-34-5Accuracy91.88Unverified
4GLOT-DRAccuracy84.13Unverified
5TRADES-ANCRA/ResNet18Accuracy81.7Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (SGD, Cosine)Accuracy77.4Unverified
2ResNet-50 (SGD, Step)Accuracy76.9Unverified
3DeiT-S (AdamW, Cosine)Accuracy76.8Unverified
4ResNet-50 (AdamW, Cosine)Accuracy76.4Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy12.2Unverified
2ResNet-50 (SGD, Cosine)Accuracy3.3Unverified
3ResNet-50 (SGD, Step)Accuracy3.2Unverified
4ResNet-50 (AdamW, Cosine)Accuracy3.1Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (AdamW, Cosine)mean Corruption Error (mCE)59.3Unverified
2ResNet-50 (SGD, Step)mean Corruption Error (mCE)57.9Unverified
3ResNet-50 (SGD, Cosine)mean Corruption Error (mCE)56.9Unverified
4DeiT-S (AdamW, Cosine)mean Corruption Error (mCE)48Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy13Unverified
2ResNet-50 (SGD, Cosine)Accuracy8.4Unverified
3ResNet-50 (SGD, Step)Accuracy8.3Unverified
4ResNet-50 (AdamW, Cosine)Accuracy8.1Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed ClassifierClean Accuracy85.21Unverified
2ResNet18/MART-ANCRAClean Accuracy60.1Unverified