SOTAVerified

Adversarial Robustness

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–50 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
Intriguing Frequency Interpretation of Adversarial Robustness for CNNs and ViTs—0
NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models—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
The Butterfly Effect in Pathology: Exploring Security in Pathology Foundation ModelsCode0
On the Scaling of Robustness and Effectiveness in Dense Retrieval—0
Model Unlearning via Sparse Autoencoder Subspace Guided Projections—0
Are classical deep neural networks weakly adversarially robust?—0
How Do Diffusion Models Improve Adversarial Robustness?—0
Your Classifier Can Do More: Towards Bridging the Gaps in Classification, Robustness, and Generation—0
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains—0
Erasing Concepts, Steering Generations: A Comprehensive Survey of Concept Suppression—0
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications—0
Curvature Dynamic Black-box Attack: revisiting adversarial robustness via dynamic curvature estimation—0
LORE: Lagrangian-Optimized Robust Embeddings for Visual EncodersCode4
Enhancing Adversarial Robustness of Vision Language Models via Adversarial Mixture Prompt Tuning—0
Experimental robustness benchmark of quantum neural network on a superconducting quantum processor—0
Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models—0
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off—0
Lessons from Defending Gemini Against Indirect Prompt Injections—0
FlowPure: Continuous Normalizing Flows for Adversarial PurificationCode1
Recommender Systems for Democracy: Toward Adversarial Robustness in Voting Advice Applications—0
Adversarial Robustness for Unified Multi-Modal Encoders via Efficient Calibration—0
Adversarially Robust Spiking Neural Networks with Sparse Connectivity—0
CARES: Comprehensive Evaluation of Safety and Adversarial Robustness in Medical LLMs—0
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation ModelsCode1
Evaluating the Robustness of Adversarial Defenses in Malware Detection SystemsCode0
Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks—0
Unpacking Robustness in Inflectional Languages: Adversarial Evaluation and Mechanistic Insights—0
ALMA: Aggregated Lipschitz Maximization Attack on Auto-encoders—0
Adversarial Robustness Analysis of Vision-Language Models in Medical Image SegmentationCode0
Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR ImagesCode0
Quantum Support Vector Regression for Robust Anomaly Detection—0
Show:102550
← PrevPage 1 of 35Next →
All datasetsAdvGLUECIFAR-10ImageNetImageNet-AImageNet-CStylized ImageNetCIFAR-100

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