SOTAVerified

Adversarial Robustness

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

Papers

Showing 1–25 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 Jailbreaking—0
Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense—0
Evaluating the Evaluators: Trust in Adversarial Robustness Tests—0
Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models—0
NIC-RobustBench: A Comprehensive Open-Source Toolkit for Neural Image Compression and Robustness AnalysisCode1
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
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-tuning—0
RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image DetectorsCode0
Dynamic Epsilon Scheduling: A Multi-Factor Adaptive Perturbation Budget for Adversarial Training—0
Speech Unlearning—0
SafeGenes: Evaluating the Adversarial Robustness of Genomic Foundation Models—0
A Flat Minima Perspective on Understanding Augmentations and Model Robustness—0
Model Unlearning via Sparse Autoencoder Subspace Guided Projections—0
On the Scaling of Robustness and Effectiveness in Dense Retrieval—0
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)Accuracy0.61—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