SOTAVerified

Adversarial Robustness

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

Papers

Showing 601650 of 1746 papers

TitleStatusHype
Adversarial robustness of amortized Bayesian inferenceCode0
Impact of Architectural Modifications on Deep Learning Adversarial RobustnessCode0
End-to-end Kernel Learning via Generative Random Fourier FeaturesCode0
Architectural Resilience to Foreground-and-Background Adversarial NoiseCode0
A Robust Backpropagation-Free Framework for ImagesCode0
Enhancing 3D Robotic Vision Robustness by Minimizing Adversarial Mutual Information through a Curriculum Training ApproachCode0
Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial NoisesCode0
Adversarial Robustness Guarantees for Gaussian ProcessesCode0
Implicit Generative Modeling of Random Noise during Training for Adversarial RobustnessCode0
Is Adversarial Training with Compressed Datasets Effective?Code0
Adversarial Robustness for Visual Grounding of Multimodal Large Language ModelsCode0
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit ConstraintsCode0
Do Perceptually Aligned Gradients Imply Adversarial Robustness?Code0
Don't Look into the Sun: Adversarial Solarization Attacks on Image ClassifiersCode0
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial PruningCode0
Hierarchical Distribution-Aware Testing of Deep LearningCode0
Enhancing Adversarial Training via Reweighting Optimization TrajectoryCode0
Gradient-Free Adversarial Attacks for Bayesian Neural NetworksCode0
Enhancing Multiple Reliability Measures via Nuisance-extended Information BottleneckCode0
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware DetectionCode0
Give me a hint: Can LLMs take a hint to solve math problems?Code0
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial RobustnessCode0
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning ApproachCode0
Global-Local Regularization Via Distributional RobustnessCode0
GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative ModelsCode0
Does language help generalization in vision models?Code0
Understanding Intrinsic Robustness Using Label UncertaintyCode0
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural NetworksCode0
Error Diffusion Halftoning Against Adversarial ExamplesCode0
An Empirical Study on the Relation between Network Interpretability and Adversarial RobustnessCode0
APRICOT: A Dataset of Physical Adversarial Attacks on Object DetectionCode0
Evading classifiers in discrete domains with provable optimality guaranteesCode0
Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial RobustnessCode0
Weight-Covariance Alignment for Adversarially Robust Neural NetworksCode0
Tight Certificates of Adversarial Robustness for Randomly Smoothed ClassifiersCode0
Physics-constrained Attack against Convolution-based Human Motion PredictionCode0
A practical approach to evaluating the adversarial distance for machine learning classifiersCode0
A Study on Adversarial Robustness of Discriminative Prototypical LearningCode0
Generating Adversarial Examples with Adversarial NetworksCode0
Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial RobustnessCode0
GenAttack: Practical Black-box Attacks with Gradient-Free OptimizationCode0
J-Guard: Journalism Guided Adversarially Robust Detection of AI-generated NewsCode0
Approximate Manifold Defense Against Multiple Adversarial PerturbationsCode0
Adversarial Concurrent Training: Optimizing Robustness and Accuracy Trade-off of Deep Neural NetworksCode0
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and MoreCode0
Evaluating the Adversarial Robustness of Semantic Segmentation: Trying Harder Pays OffCode0
Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR ImagesCode0
Language Guided Adversarial PurificationCode0
Disentangling Adversarial Robustness in Directions of the Data ManifoldCode0
Disentangling Adversarial Robustness and GeneralizationCode0
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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