SOTAVerified

Adversarial Robustness

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

Papers

Showing 301–350 of 1746 papers

TitleStatusHype
Adversarial Vertex Mixup: Toward Better Adversarially Robust GeneralizationCode1
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacksCode1
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial RobustnessCode1
Attacks Which Do Not Kill Training Make Adversarial Learning StrongerCode1
Learning Adversarially Robust Representations via Worst-Case Mutual Information MaximizationCode1
Hold me tight! Influence of discriminative features on deep network boundariesCode1
Adversarial Robustness for CodeCode1
Random Smoothing Might be Unable to Certify _ Robustness for High-Dimensional ImagesCode1
Renofeation: A Simple Transfer Learning Method for Improved Adversarial RobustnessCode1
Towards Sharper First-Order Adversary with Quantized GradientsCode1
Adversarial Robustness Against the Union of Multiple Threat ModelsCode1
Explainability and Adversarial Robustness for RNNsCode1
Universal Adversarial Robustness of Texture and Shape-Biased ModelsCode1
Adversarial Robustness Against the Union of Multiple Perturbation ModelsCode1
MNIST-C: A Robustness Benchmark for Computer VisionCode1
Adversarial Robustness as a Prior for Learned RepresentationsCode1
Adversarially Robust DistillationCode1
Wasserstein Adversarial Examples via Projected Sinkhorn IterationsCode1
On Evaluating Adversarial RobustnessCode1
Certified Adversarial Robustness via Randomized SmoothingCode1
Improving Adversarial Robustness via Promoting Ensemble DiversityCode1
Theoretically Principled Trade-off between Robustness and AccuracyCode1
Robustness May Be at Odds with AccuracyCode1
Towards Deep Learning Models Resistant to Adversarial AttacksCode1
Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix ApproachCode0
Tail-aware Adversarial Attacks: A Distributional Approach to Efficient LLM Jailbreaking—0
Evaluating the Evaluators: Trust in Adversarial Robustness Tests—0
Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense—0
Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models—0
PRISON: Unmasking the Criminal Potential of Large Language Models—0
NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models—0
Intriguing Frequency Interpretation of Adversarial Robustness for CNNs and ViTs—0
Canonical Latent Representations in Conditional Diffusion Models—0
Towards Class-wise Fair Adversarial Training via Anti-Bias Soft Label DistillationCode0
The interplay of robustness and generalization in quantum machine learningCode0
ProARD: progressive adversarial robustness distillation: provide wide range of robust studentsCode0
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model ReliabilityCode0
RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image DetectorsCode0
Sylva: Tailoring Personalized Adversarial Defense in Pre-trained Models via Collaborative Fine-tuning—0
Dynamic Epsilon Scheduling: A Multi-Factor Adaptive Perturbation Budget for Adversarial Training—0
SafeGenes: Evaluating the Adversarial Robustness of Genomic Foundation Models—0
Speech Unlearning—0
Model Unlearning via Sparse Autoencoder Subspace Guided Projections—0
A Flat Minima Perspective on Understanding Augmentations and Model Robustness—0
On the Scaling of Robustness and Effectiveness in Dense Retrieval—0
The Butterfly Effect in Pathology: Exploring Security in Pathology Foundation ModelsCode0
How Do Diffusion Models Improve Adversarial Robustness?—0
Are classical deep neural networks weakly adversarially robust?—0
Erasing Concepts, Steering Generations: A Comprehensive Survey of Concept Suppression—0
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeBERTa (single model)Accuracy0.61—Unverified
2ALBERT (single model)Accuracy0.59—Unverified
3T5 (single model)Accuracy0.57—Unverified
4SMART_RoBERTa (single model)Accuracy0.54—Unverified
5FreeLB (single model)Accuracy0.5—Unverified
6RoBERTa (single model)Accuracy0.5—Unverified
7InfoBERT (single model)Accuracy0.46—Unverified
8ELECTRA (single model)Accuracy0.42—Unverified
9BERT (single model)Accuracy0.34—Unverified
10SMART_BERT (single model)Accuracy0.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed classifierAccuracy95.23—Unverified
2Stochastic-LWTA/PGD/WideResNet-34-10Accuracy92.26—Unverified
3Stochastic-LWTA/PGD/WideResNet-34-5Accuracy91.88—Unverified
4GLOT-DRAccuracy84.13—Unverified
5TRADES-ANCRA/ResNet18Accuracy81.7—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (SGD, Cosine)Accuracy77.4—Unverified
2ResNet-50 (SGD, Step)Accuracy76.9—Unverified
3DeiT-S (AdamW, Cosine)Accuracy76.8—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy76.4—Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy12.2—Unverified
2ResNet-50 (SGD, Cosine)Accuracy3.3—Unverified
3ResNet-50 (SGD, Step)Accuracy3.2—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy3.1—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (AdamW, Cosine)mean Corruption Error (mCE)59.3—Unverified
2ResNet-50 (SGD, Step)mean Corruption Error (mCE)57.9—Unverified
3ResNet-50 (SGD, Cosine)mean Corruption Error (mCE)56.9—Unverified
4DeiT-S (AdamW, Cosine)mean Corruption Error (mCE)48—Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy13—Unverified
2ResNet-50 (SGD, Cosine)Accuracy8.4—Unverified
3ResNet-50 (SGD, Step)Accuracy8.3—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy8.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed ClassifierClean Accuracy85.21—Unverified
2ResNet18/MART-ANCRAClean Accuracy60.1—Unverified