SOTAVerified

Adversarial Robustness

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

Papers

Showing 101150 of 1746 papers

TitleStatusHype
Enhancing Adversarial Robustness via Test-time Transformation EnsemblingCode1
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid DecoderCode1
Explainability and Adversarial Robustness for RNNsCode1
Explainability-Aware One Point Attack for Point Cloud Neural NetworksCode1
Exploring and Exploiting Decision Boundary Dynamics for Adversarial RobustnessCode1
Exploring Architectural Ingredients of Adversarially Robust Deep Neural NetworksCode1
AdvDrop: Adversarial Attack to DNNs by Dropping InformationCode1
Are Transformers More Robust Than CNNs?Code1
Achieving robustness in classification using optimal transport with hinge regularizationCode1
Adversarial Machine Learning: Bayesian PerspectivesCode1
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation ModelsCode1
Fixing Data Augmentation to Improve Adversarial RobustnessCode1
FlowPure: Continuous Normalizing Flows for Adversarial PurificationCode1
Clipped Hyperbolic Classifiers Are Super-Hyperbolic ClassifiersCode1
Adversarial Attack and Defense in Deep RankingCode1
Adversarial Prompt Tuning for Vision-Language ModelsCode1
Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial RobustnessCode1
Adversarial Reasoning at Jailbreaking TimeCode1
Adversarial Attack on Deep Learning-Based Splice LocalizationCode1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical FlowCode1
Adversarial Robustification via Text-to-Image Diffusion ModelsCode1
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
Hold me tight! Influence of discriminative features on deep network boundariesCode1
Adversarial Image Color Transformations in Explicit Color Filter SpaceCode1
Adversarial Robustness against Multiple and Single l_p-Threat Models via Quick Fine-Tuning of Robust ClassifiersCode1
Adversarial Robustness Against the Union of Multiple Perturbation ModelsCode1
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty DetectionCode1
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural NetworksCode1
Adversarial Robustness for CodeCode1
A Self-supervised Approach for Adversarial RobustnessCode1
Adversarial Attacks on ML Defense Models CompetitionCode1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNsCode1
Adversarial Vulnerability of Randomized EnsemblesCode1
Adversarial Visual Robustness by Causal InterventionCode1
Adversarial Vertex Mixup: Toward Better Adversarially Robust GeneralizationCode1
Adversarial vulnerability of powerful near out-of-distribution detectionCode1
A Light Recipe to Train Robust Vision TransformersCode1
An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN ArchitecturesCode1
AdvRush: Searching for Adversarially Robust Neural ArchitecturesCode1
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal ClassificationCode1
Are socially-aware trajectory prediction models really socially-aware?Code1
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-TuningCode1
Adversarial Contrastive Learning via Asymmetric InfoNCECode1
Attacks Which Do Not Kill Training Make Adversarial Learning StrongerCode1
Adversarial Robustness via Random Projection FiltersCode1
Adversarial Robustness as a Prior for Learned RepresentationsCode1
Adversarial Robustness in Graph Neural Networks: A Hamiltonian ApproachCode1
Adversarial Robustness of Deep Convolutional Candlestick LearnerCode1
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World AttacksCode1
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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