SOTAVerified

Adversarial Robustness

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

Papers

Showing 15511600 of 1746 papers

TitleStatusHype
DeMem: Privacy-Enhanced Robust Adversarial Learning via De-MemorizationCode0
Weight-Covariance Alignment for Adversarially Robust Neural NetworksCode0
Targeted Adversarial Attacks on Wind Power ForecastsCode0
J-Guard: Journalism Guided Adversarially Robust Detection of AI-generated NewsCode0
Defending Adversarial Examples by Negative Correlation EnsembleCode0
Simple Post-Training Robustness Using Test Time Augmentations and Random ForestCode0
Reproducibility Study on Adversarial Attacks Against Robust Transformer TrackersCode0
KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge GraphsCode0
k-Mixup Regularization for Deep Learning via Optimal TransportCode0
ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement LearningCode0
Targeted View-Invariant Adversarial Perturbations for 3D Object RecognitionCode0
Towards Theoretical Analysis of Transformation Complexity of ReLU DNNsCode0
Kolmogorov-Arnold Networks (KAN) for Time Series Classification and Robust AnalysisCode0
Unlabeled Data Improves Adversarial RobustnessCode0
What it Thinks is Important is Important: Robustness Transfers through Input GradientsCode0
Language Guided Adversarial PurificationCode0
Large Language Model Assisted Adversarial Robustness Neural Architecture SearchCode0
Assaying Out-Of-Distribution Generalization in Transfer LearningCode0
Deep Defense: Training DNNs with Improved Adversarial RobustnessCode0
Large Norms of CNN Layers Do Not Hurt Adversarial RobustnessCode0
LADDER: Latent Boundary-guided Adversarial TrainingCode0
Latent Feature Relation Consistency for Adversarial RobustnessCode0
Adversarial Robustness of Neural-Statistical Features in Detection of Generative TransformersCode0
DeepBern-Nets: Taming the Complexity of Certifying Neural Networks using Bernstein Polynomial Activations and Precise Bound PropagationCode0
Deep anytime-valid hypothesis testingCode0
Learning Diverse-Structured Networks for Adversarial RobustnessCode0
Learning Robust 3D Representation from CLIP via Dual DenoisingCode0
Learning Robust and Privacy-Preserving Representations via Information TheoryCode0
Adversarial Robustness of MR Image Reconstruction under Realistic PerturbationsCode0
Rethinking Empirical Evaluation of Adversarial Robustness Using First-Order Attack MethodsCode0
A Brain-Inspired Regularizer for Adversarial RobustnessCode0
The Butterfly Effect in Pathology: Exploring Security in Pathology Foundation ModelsCode0
Towards Robust Neural Networks via Orthogonal DiversityCode0
Rethinking Softmax Cross-Entropy Loss for Adversarial RobustnessCode0
Towards Unified Robustness Against Both Backdoor and Adversarial AttacksCode0
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial RobustnessCode0
A Simple Approach to Adversarial Robustness in Few-shot Image ClassificationCode0
LightPure: Realtime Adversarial Image Purification for Mobile Devices Using Diffusion ModelsCode0
WARLearn: Weather-Adaptive Representation LearningCode0
Exploring the Landscape of Spatial RobustnessCode0
Revisiting DeepFool: generalization and improvementCode0
AROID: Improving Adversarial Robustness Through Online Instance-Wise Data AugmentationCode0
A Robust Backpropagation-Free Framework for ImagesCode0
Are Large Language Models Really Bias-Free? Jailbreak Prompts for Assessing Adversarial Robustness to Bias ElicitationCode0
Are Labels Required for Improving Adversarial Robustness?Code0
Unsupervised Adversarial Detection without Extra Model: Training Loss Should ChangeCode0
Logit Pairing Methods Can Fool Gradient-Based AttacksCode0
Adversarial Robustness of Deep Learning-Based Malware Detectors via (De)Randomized SmoothingCode0
Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification TasksCode0
Deceptive Fairness Attacks on Graphs via Meta LearningCode0
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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