SOTAVerified

Adversarial Robustness

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

Papers

Showing 201250 of 1746 papers

TitleStatusHype
CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision ModelsCode1
Multitask Learning Strengthens Adversarial RobustnessCode1
Adversarial Robustness via Random Projection FiltersCode1
CBA: Contextual Background Attack against Optical Aerial Detection in the Physical WorldCode1
NIC-RobustBench: A Comprehensive Open-Source Toolkit for Neural Image Compression and Robustness AnalysisCode1
Cauchy-Schwarz Divergence Information Bottleneck for RegressionCode1
On Evaluating Adversarial Robustness of Volumetric Medical Segmentation ModelsCode1
Distance Learner: Incorporating Manifold Prior to Model TrainingCode1
Certified Adversarial Robustness via Randomized SmoothingCode1
(Certified!!) Adversarial Robustness for Free!Code1
Certified Training: Small Boxes are All You NeedCode1
Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion ModelsCode1
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature SelectionCode1
CFA: Class-wise Calibrated Fair Adversarial TrainingCode1
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World AttacksCode1
AdvDrop: Adversarial Attack to DNNs by Dropping InformationCode1
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative AttacksCode1
Adversarial Vertex Mixup: Toward Better Adversarially Robust GeneralizationCode1
Adversarial Visual Robustness by Causal InterventionCode1
Adversarial vulnerability of powerful near out-of-distribution detectionCode1
Adversarial Vulnerability of Randomized EnsemblesCode1
Adversarial Machine Learning: Bayesian PerspectivesCode1
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation ModelsCode1
Composite Adversarial AttacksCode1
PeerAiD: Improving Adversarial Distillation from a Specialized Peer TutorCode1
AdvRush: Searching for Adversarially Robust Neural ArchitecturesCode1
Consistency Regularization for Adversarial RobustnessCode1
PixMix: Dreamlike Pictures Comprehensively Improve Safety MeasuresCode1
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion DetectorsCode1
Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial RobustnessCode1
Adversarial Attack and Defense in Deep RankingCode1
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasksCode1
Adversarial Prompt Tuning for Vision-Language ModelsCode1
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric LearningCode1
ExCon: Explanation-driven Supervised Contrastive Learning for Image ClassificationCode1
Adversarial Attack and Defense Strategies for Deep Speaker Recognition SystemsCode1
Adversarial Reasoning at Jailbreaking TimeCode1
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine LearningCode1
Adversarial Attack on Deep Learning-Based Splice LocalizationCode1
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial RemovalCode1
LyaNet: A Lyapunov Framework for Training Neural ODEsCode1
Regularization with Latent Space Virtual Adversarial TrainingCode1
A Light Recipe to Train Robust Vision TransformersCode1
Adversarial Robustification via Text-to-Image Diffusion ModelsCode1
Decoupled Kullback-Leibler Divergence LossCode1
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial RobustnessCode1
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm RegularizationCode1
An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN ArchitecturesCode1
Adversarial Image Color Transformations in Explicit Color Filter SpaceCode1
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic CurriculumCode1
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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