SOTAVerified

Adversarial Robustness

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

Papers

Showing 125 of 1746 papers

TitleStatusHype
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 Jailbreaking0
Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense0
Evaluating the Evaluators: Trust in Adversarial Robustness Tests0
Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models0
NIC-RobustBench: A Comprehensive Open-Source Toolkit for Neural Image Compression and Robustness AnalysisCode1
PRISON: Unmasking the Criminal Potential of Large Language Models0
NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models0
Intriguing Frequency Interpretation of Adversarial Robustness for CNNs and ViTs0
Canonical Latent Representations in Conditional Diffusion Models0
PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo AnomaliesCode1
Towards Class-wise Fair Adversarial Training via Anti-Bias Soft Label DistillationCode0
The interplay of robustness and generalization in quantum machine learningCode0
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model ReliabilityCode0
ProARD: progressive adversarial robustness distillation: provide wide range of robust studentsCode0
Sylva: Tailoring Personalized Adversarial Defense in Pre-trained Models via Collaborative Fine-tuning0
RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image DetectorsCode0
Dynamic Epsilon Scheduling: A Multi-Factor Adaptive Perturbation Budget for Adversarial Training0
Speech Unlearning0
SafeGenes: Evaluating the Adversarial Robustness of Genomic Foundation Models0
A Flat Minima Perspective on Understanding Augmentations and Model Robustness0
Model Unlearning via Sparse Autoencoder Subspace Guided Projections0
On the Scaling of Robustness and Effectiveness in Dense Retrieval0
The Butterfly Effect in Pathology: Exploring Security in Pathology Foundation ModelsCode0
Are classical deep neural networks weakly adversarially robust?0
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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