SOTAVerified

Adversarial Robustness

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

Papers

Showing 17011746 of 1746 papers

TitleStatusHype
Sorting out Lipschitz function approximationCode0
Theoretical Analysis of Adversarial Learning: A Minimax Approach0
New CleverHans Feature: Better Adversarial Robustness Evaluations with Attack Bundling0
Improving Adversarial Robustness by Encouraging Discriminative Features0
On the Effectiveness of Minimal Context Selection for Robust Question Answering0
Logit Pairing Methods Can Fool Gradient-Based AttacksCode0
Towards Robust Deep Neural Networks0
Improving Document Binarization via Adversarial Noise-Texture AugmentationCode0
Evading classifiers in discrete domains with provable optimality guaranteesCode0
Sparse DNNs with Improved Adversarial Robustness0
Average Margin Regularization for Classifiers0
Generalized No Free Lunch Theorem for Adversarial Robustness0
Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness0
Improved robustness to adversarial examples using Lipschitz regularization of the lossCode0
Knowledge-guided Semantic Computing Network0
Interpreting Adversarial Robustness: A View from Decision Surface in Input Space0
CAAD 2018: Generating Transferable Adversarial ExamplesCode0
Distilled Agent DQN for Provable Adversarial Robustness0
Certified Adversarial Robustness with Additive NoiseCode0
Training for Faster Adversarial Robustness Verification via Inducing ReLU StabilityCode0
Lipschitz regularized Deep Neural Networks generalize and are adversarially robust0
Implicit Generative Modeling of Random Noise during Training for Adversarial RobustnessCode0
Adversarial Robustness Toolbox v1.0.0Code3
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks0
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural NetworksCode0
Robustness May Be at Odds with AccuracyCode1
GenAttack: Practical Black-box Attacks with Gradient-Free OptimizationCode0
Towards the first adversarially robust neural network model on MNISTCode0
Deep Defense: Training DNNs with Improved Adversarial RobustnessCode0
Are Generative Classifiers More Robust to Adversarial Attacks?Code0
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks0
A3T: Adversarially Augmented Adversarial Training0
Generating Adversarial Examples with Adversarial NetworksCode0
Facial Attributes: Accuracy and Adversarial Robustness0
Exploring the Landscape of Spatial RobustnessCode0
Layer-wise Learning of Stochastic Neural Networks with Information Bottleneck0
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input GradientsCode0
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training0
Intriguing Properties of Adversarial Examples0
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks0
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial ExamplesCode0
Towards Proving the Adversarial Robustness of Deep Neural Networks0
Adversarial Robustness: Softmax versus Openmax0
Towards Deep Learning Models Resistant to Adversarial AttacksCode1
A Closer Look at Memorization in Deep NetworksCode0
LOTS about Attacking Deep Features0
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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