SOTAVerified

Adversarial Robustness

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

Papers

Showing 16511700 of 1746 papers

TitleStatusHype
Adversarial Robustness Guarantees for Classification with Gaussian ProcessesCode0
Scaleable input gradient regularization for adversarial robustnessCode0
Non-Determinism in Neural Networks for Adversarial Robustness0
Rethinking Softmax Cross-Entropy Loss for Adversarial RobustnessCode0
Power up! Robust Graph Convolutional Network via Graph PoweringCode0
Adversarially Robust DistillationCode1
What Do Adversarially Robust Models Look At?Code0
On Adversarial Robustness of Small vs Large Batch Training0
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks0
On the Connection Between Adversarial Robustness and Saliency Map InterpretabilityCode0
Exploring the Hyperparameter Landscape of Adversarial Robustness0
A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks0
An Empirical Evaluation of Adversarial Robustness under Transfer Learning0
Transfer of Adversarial Robustness Between Perturbation Types0
Dropping Pixels for Adversarial Robustness0
Don't let your Discriminator be fooled0
On Meaning-Preserving Adversarial Perturbations for Sequence-to-Sequence Models0
Prototypical Examples in Deep Learning: Metrics, Characteristics, and Utility0
Evaluation Methodology for Attacks Against Confidence Thresholding Models0
Adversarial Training and Robustness for Multiple PerturbationsCode0
Interpreting Adversarial Examples by Activation Promotion and Suppression0
Adversarial Robustness vs Model Compression, or Both?Code0
On the Adversarial Robustness of Multivariate Robust Estimation0
Bridging Adversarial Robustness and Gradient InterpretabilityCode0
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness0
Improving Adversarial Robustness via Guided Complement EntropyCode0
On Evaluation of Adversarial Perturbations for Sequence-to-Sequence ModelsCode0
On the Effectiveness of Low Frequency Perturbations0
On the Sensitivity of Adversarial Robustness to Input Data Distributions0
Wasserstein Adversarial Examples via Projected Sinkhorn IterationsCode1
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorchCode0
On Evaluating Adversarial RobustnessCode1
Model Compression with Adversarial Robustness: A Unified Optimization FrameworkCode0
Certified Adversarial Robustness via Randomized SmoothingCode1
Discretization based Solutions for Secure Machine Learning against Adversarial Attacks0
Robustness Of Saak Transform Against Adversarial Attacks0
Theoretical evidence for adversarial robustness through randomizationCode0
Adversarial Examples Are a Natural Consequence of Test Error in Noise0
On the Effect of Low-Rank Weights on Adversarial Robustness of Neural Networks0
Improving Adversarial Robustness of Ensembles with Diversity Training0
Using Pre-Training Can Improve Model Robustness and UncertaintyCode0
Improving Adversarial Robustness via Promoting Ensemble DiversityCode1
Theoretically Principled Trade-off between Robustness and AccuracyCode1
Adversarial Robustness May Be at Odds With Simplicity0
Increasing the adversarial robustness and explainability of capsule networks with γ-capsulesCode0
Feature Denoising for Improving Adversarial RobustnessCode0
MMA Training: Direct Input Space Margin Maximization through Adversarial TrainingCode0
Disentangling Adversarial Robustness and GeneralizationCode0
Effects of Loss Functions And Target Representations on Adversarial Robustness0
Robustness via curvature regularization, and vice versaCode0
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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