SOTAVerified

Adversarial Robustness

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

Papers

Showing 151175 of 1746 papers

TitleStatusHype
Cauchy-Schwarz Divergence Information Bottleneck for RegressionCode1
CBA: Contextual Background Attack against Optical Aerial Detection in the Physical WorldCode1
Certified Adversarial Robustness via Randomized SmoothingCode1
Certified Training: Small Boxes are All You NeedCode1
Adversarial Robustness Limits via Scaling-Law and Human-Alignment StudiesCode1
Adversarial Robustness as a Prior for Learned RepresentationsCode1
Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented CommunicationCode1
Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial AttacksCode1
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric LearningCode1
On the Adversarial Robustness of Vision TransformersCode1
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial RemovalCode1
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial RobustnessCode1
Adversarial Robustness under Long-Tailed DistributionCode1
RobFR: Benchmarking Adversarial Robustness on Face RecognitionCode1
An Embarrassingly Simple Backdoor Attack on Self-supervised LearningCode1
Demystify Transformers & Convolutions in Modern Image Deep NetworksCode1
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized NetworksCode1
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust ExplorationCode1
Efficient Image-to-Image Diffusion Classifier for Adversarial RobustnessCode1
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified RobustnessCode1
Adversarial Robustness via Random Projection FiltersCode1
Engineering flexible machine learning systems by traversing functionally-invariant pathsCode1
Enhancing adversarial robustness in Natural Language Inference using explanationsCode1
Enhancing Adversarial Robustness via Score-Based OptimizationCode1
Adversarial Attacks on Graph Classifiers via Bayesian OptimisationCode1
Show:102550
← PrevPage 7 of 70Next →

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