SOTAVerified

Adversarial Robustness

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

Papers

Showing 51100 of 1746 papers

TitleStatusHype
Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World AttacksCode1
Exploring Adversarial Robustness of Deep State Space ModelsCode1
Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular DataCode1
White-box Multimodal Jailbreaks Against Large Vision-Language ModelsCode1
Universal Adversarial Perturbations for Vision-Language Pre-trained ModelsCode1
Cauchy-Schwarz Divergence Information Bottleneck for RegressionCode1
Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion ModelsCode1
Adversarial Robustness Limits via Scaling-Law and Human-Alignment StudiesCode1
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples RegularizationCode1
BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression TasksCode1
Few-Shot Adversarial Prompt Learning on Vision-Language ModelsCode1
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm RegularizationCode1
PeerAiD: Improving Adversarial Distillation from a Specialized Peer TutorCode1
Speech Robust Bench: A Robustness Benchmark For Speech RecognitionCode1
DF-RAP: A Robust Adversarial Perturbation for Defending against Deepfakes in Real-world Social Network ScenariosCode1
On the Duality Between Sharpness-Aware Minimization and Adversarial TrainingCode1
Stop Reasoning! When Multimodal LLM with Chain-of-Thought Reasoning Meets Adversarial ImageCode1
Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM AssessmentCode1
Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding SpaceCode1
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-errorCode1
σ-zero: Gradient-based Optimization of _0-norm Adversarial ExamplesCode1
The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical ImagesCode1
Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial RobustnessCode1
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMsCode1
Adversarial Prompt Tuning for Vision-Language ModelsCode1
Multi-scale Diffusion Denoised SmoothingCode1
OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution ShiftCode1
IRAD: Implicit Representation-driven Image Resampling against Adversarial AttacksCode1
To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For NowCode1
Improving Fast Minimum-Norm Attacks with Hyperparameter OptimizationCode1
Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial AttacksCode1
Adversarial Robustness in Graph Neural Networks: A Hamiltonian ApproachCode1
VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained ModelsCode1
How Robust is Google's Bard to Adversarial Image Attacks?Code1
Robust Principles: Architectural Design Principles for Adversarially Robust CNNsCode1
Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight AveragingCode1
On the Adversarial Robustness of Multi-Modal Foundation ModelsCode1
HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing ThresholdsCode1
Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain PromptingCode1
Robust Mixture-of-Expert Training for Convolutional Neural NetworksCode1
TrajPAC: Towards Robustness Verification of Pedestrian Trajectory Prediction ModelsCode1
Large Language Models to Identify Social Determinants of Health in Electronic Health RecordsCode1
Benchmarking and Analyzing Robust Point Cloud Recognition: Bag of Tricks for Defending Adversarial ExamplesCode1
HybridAugment++: Unified Frequency Spectra Perturbations for Model RobustnessCode1
Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine LearningCode1
Enhancing Adversarial Robustness via Score-Based OptimizationCode1
Mitigating Accuracy-Robustness Trade-off via Balanced Multi-Teacher Adversarial DistillationCode1
Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial TrainingCode1
Similarity Preserving Adversarial Graph Contrastive LearningCode1
Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation ModelsCode1
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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