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 1651–1700 of 1746 papers

TitleStatusHype
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models—0
Exposing the Robustness and Vulnerability of Hybrid 8T-6T SRAM Memory Architectures to Adversarial Attacks in Deep Neural Networks—0
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs—0
CARES: Comprehensive Evaluation of Safety and Adversarial Robustness in Medical LLMs—0
Extreme Miscalibration and the Illusion of Adversarial Robustness—0
F^2AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns—0
Facial Attributes: Accuracy and Adversarial Robustness—0
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices—0
FADER: Fast Adversarial Example Rejection—0
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks—0
Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin—0
Sparse DNNs with Improved Adversarial Robustness—0
Fair Robust Active Learning by Joint Inconsistency—0
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training—0
Faithful Knowledge Distillation—0
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer—0
Understanding Adversarial Robustness Through Loss Landscape Geometries—0
Fast Adversarial Training against Textual Adversarial Attacks—0
Understanding and Measuring Robustness of Multimodal Learning—0
Fast Adversarial Training with Weak-to-Strong Spatial-Temporal Consistency in the Frequency Domain on Videos—0
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification—0
SpeechGuard: Exploring the Adversarial Robustness of Multimodal Large Language Models—0
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off—0
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective—0
FAT: Federated Adversarial Training—0
Fault Tolerance of Neural Networks in Adversarial Settings—0
Feature Averaging: An Implicit Bias of Gradient Descent Leading to Non-Robustness in Neural Networks—0
Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness—0
Adversarial Information Bottleneck—0
Feature Distillation With Guided Adversarial Contrastive Learning—0
Feature Losses for Adversarial Robustness—0
Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness—0
Better Generalization with Adaptive Adversarial Training—0
Speech Unlearning—0
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection—0
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks—0
FedProphet: Memory-Efficient Federated Adversarial Training via Theoretic-Robustness and Low-Inconsistency Cascade Learning—0
Feedback Learning for Improving the Robustness of Neural Networks—0
Fermi-Bose Machine achieves both generalization and adversarial robustness—0
Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models—0
Benchmarking Adversarial Robustness of Compressed Deep Learning Models—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
Finding a human-like classifier—0
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness—0
Finding Dynamics Preserving Adversarial Winning Tickets—0
Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering—0
SPROUT: Self-Progressing Robust Training—0
Fixed Inter-Neuron Covariability Induces Adversarial Robustness—0
Benchmarking Adversarial Robustness—0
A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models—0
Show:102550
← PrevPage 34 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