SOTAVerified

Adversarial Robustness

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

Papers

Showing 150 of 1746 papers

TitleStatusHype
AugLy: Data Augmentations for RobustnessCode5
LORE: Lagrangian-Optimized Robust Embeddings for Visual EncodersCode4
Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoningCode3
Adversarial Robustness Toolbox v1.0.0Code3
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial ExamplesCode3
Improving Alignment and Robustness with Circuit BreakersCode3
An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare RecordsCode2
RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text DetectorsCode2
ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red TeamingCode2
Fast Minimum-norm Adversarial Attacks through Adaptive Norm ConstraintsCode2
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language ModelsCode2
Authorship Obfuscation in Multilingual Machine-Generated Text DetectionCode2
On Evaluating Adversarial Robustness of Large Vision-Language ModelsCode2
A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and RecommendationsCode2
Artificial Kuramoto Oscillatory NeuronsCode2
Dissecting Adversarial Robustness of Multimodal LM AgentsCode2
CLAIMED, a visual and scalable component library for Trusted AICode2
MIBench: A Comprehensive Framework for Benchmarking Model Inversion Attack and DefenseCode2
AdvRush: Searching for Adversarially Robust Neural ArchitecturesCode1
Adversarial Visual Robustness by Causal InterventionCode1
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric LearningCode1
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative AttacksCode1
Adversarial Robustness under Long-Tailed DistributionCode1
Adversarially Robust DistillationCode1
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World AttacksCode1
Achieving robustness in classification using optimal transport with hinge regularizationCode1
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation ModelsCode1
Adversarial Machine Learning: Bayesian PerspectivesCode1
Adversarial vulnerability of powerful near out-of-distribution detectionCode1
Adversarial Vulnerability of Randomized EnsemblesCode1
Adversarially-Aware Robust Object DetectorCode1
Adversarial Robustness via Random Projection FiltersCode1
Adversarial Vertex Mixup: Toward Better Adversarially Robust GeneralizationCode1
Adversarial Robustness of Representation Learning for Knowledge GraphsCode1
Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented CommunicationCode1
Adversarial Robustness on In- and Out-Distribution Improves ExplainabilityCode1
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-TuningCode1
Adversarial Contrastive Learning via Asymmetric InfoNCECode1
On the Adversarial Robustness of Vision TransformersCode1
Adversarial Robustness Against the Union of Multiple Threat ModelsCode1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNsCode1
Adversarial Robustness for CodeCode1
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality MetricsCode1
Adversarial Robustness Limits via Scaling-Law and Human-Alignment StudiesCode1
Adversarial Robustness of Deep Convolutional Candlestick LearnerCode1
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified RobustnessCode1
Adversarial Robustness in Graph Neural Networks: A Hamiltonian ApproachCode1
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language ModelsCode1
Adversarial Prompt Tuning for Vision-Language ModelsCode1
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
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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