SOTAVerified

Adversarial Robustness

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

Papers

Showing 14011450 of 1746 papers

TitleStatusHype
Towards Evaluating the Robustness of Neural Networks Learned by TransductionCode0
Physics-constrained Attack against Convolution-based Human Motion PredictionCode0
Boosting Adversarial Robustness using Feature Level Stochastic SmoothingCode0
Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial RobustnessCode0
Evading classifiers in discrete domains with provable optimality guaranteesCode0
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic ProgrammingCode0
Prediction without Preclusion: Recourse Verification with Reachable SetsCode0
Biologically Inspired Mechanisms for Adversarial RobustnessCode0
ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine LearningCode0
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional NetworksCode0
How to compare adversarial robustness of classifiers from a global perspectiveCode0
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual AttacksCode0
Error Diffusion Halftoning Against Adversarial ExamplesCode0
ProARD: progressive adversarial robustness distillation: provide wide range of robust studentsCode0
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural NetworksCode0
Enhancing Multiple Reliability Measures via Nuisance-extended Information BottleneckCode0
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial DefenseCode0
Adversarial Machine Learning in Latent Representations of Neural NetworksCode0
Enhancing Adversarial Training via Reweighting Optimization TrajectoryCode0
Adversarial Robustness via Fisher-Rao RegularizationCode0
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model ReliabilityCode0
Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial NoisesCode0
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge DistillationCode0
Enhancing 3D Robotic Vision Robustness by Minimizing Adversarial Mutual Information through a Curriculum Training ApproachCode0
Projected Randomized Smoothing for Certified Adversarial RobustnessCode0
Sorting out Lipschitz function approximationCode0
Measuring Adversarial Robustness using a Voronoi-Epsilon AdversaryCode0
SPADE: A Spectral Method for Black-Box Adversarial Robustness EvaluationCode0
Hyper-parameter Tuning for Adversarially Robust ModelsCode0
End-to-end Kernel Learning via Generative Random Fourier FeaturesCode0
IBP Regularization for Verified Adversarial Robustness via Branch-and-BoundCode0
IB-RAR: Information Bottleneck as Regularizer for Adversarial RobustnessCode0
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image ClassifiersCode0
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic DefensesCode0
SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam DetectionCode0
Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on VideosCode0
Characterizing Data Point Vulnerability via Average-Case RobustnessCode0
Model Compression with Adversarial Robustness: A Unified Optimization FrameworkCode0
Impact of Architectural Modifications on Deep Learning Adversarial RobustnessCode0
Provable Adversarial Robustness for Fractional Lp Threat ModelsCode0
Towards Out-of-Distribution Adversarial RobustnessCode0
Spectral regularization for adversarially-robust representation learningCode0
Spectrum Extraction and Clipping for Implicitly Linear LayersCode0
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual ExplanationsCode0
Implicit Generative Modeling of Random Noise during Training for Adversarial RobustnessCode0
Provably Bounding Neural Network PreimagesCode0
Provably Robust Boosted Decision Stumps and Trees against Adversarial AttacksCode0
Adversarially Robust Spiking Neural Networks Through ConversionCode0
Improved Adversarial Training Through Adaptive Instance-wise Loss SmoothingCode0
Adversarial Attack Generation Empowered by Min-Max OptimizationCode0
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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