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
Adversarial Robustness for Deep Learning-based Wildfire Prediction Models0
Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness0
Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning0
Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Field0
Evaluating the Adversarial Robustness of Detection Transformers0
Efficient Contrastive Explanations on DemandCode0
On the Local Complexity of Linear Regions in Deep ReLU Networks0
Adversarial Robustness through Dynamic Ensemble Learning0
Training Graph Neural Networks Using Non-Robust Samples0
Holistic Adversarially Robust Pruning0
Targeted View-Invariant Adversarial Perturbations for 3D Object RecognitionCode0
Human-in-the-Loop Generation of Adversarial Texts: A Case Study on Tibetan ScriptCode1
A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding0
Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic SegmentationCode0
Learning Robust and Privacy-Preserving Representations via Information TheoryCode0
Improving Graph Neural Networks via Adversarial Robustness Evaluation0
On Adversarial Robustness and Out-of-Distribution Robustness of Large Language ModelsCode0
Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented CommunicationCode1
Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending against Poisoning Attacks0
DeMem: Privacy-Enhanced Robust Adversarial Learning via De-MemorizationCode0
Understanding the Impact of Graph Reduction on Adversarial Robustness in Graph Neural Networks0
Nearly Solved? Robust Deepfake Detection Requires More than Visual Forensics0
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing0
TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual SimilarityCode0
IQA-Adapter: Exploring Knowledge Transfer from Image Quality Assessment to Diffusion-based Generative ModelsCode1
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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