SOTAVerified

Adversarial Robustness

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

Papers

Showing 451500 of 1746 papers

TitleStatusHype
Dense Hopfield Networks in the Teacher-Student SettingCode0
Data-Driven Subsampling in the Presence of an Adversarial ActorCode0
FullLoRA-AT: Efficiently Boosting the Robustness of Pretrained Vision Transformers0
Random Entangled Tokens for Adversarially Robust Vision Transformer0
CausalPC: Improving the Robustness of Point Cloud Classification by Causal Effect Identification0
Robust Distillation via Untargeted and Targeted Intermediate Adversarial Samples0
Towards Understanding and Improving Adversarial Robustness of Vision Transformers0
Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners0
Towards adversarial robustness verification of no-reference image-and video-quality metricsCode0
Robust Survival Analysis with Adversarial Regularization0
ARBiBench: Benchmarking Adversarial Robustness of Binarized Neural Networks0
Scaling Compute Is Not All You Need for Adversarial RobustnessCode0
LRS: Enhancing Adversarial Transferability through Lipschitz Regularized SurrogateCode0
The Pros and Cons of Adversarial Robustness0
The Ultimate Combo: Boosting Adversarial Example Transferability by Composing Data AugmentationsCode0
Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off0
Adversarial Robustness on Image Classification with k-means0
Universal Adversarial Framework to Improve Adversarial Robustness for Diabetic Retinopathy Detection0
ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement LearningCode0
Initialization Matters for Adversarial Transfer LearningCode0
Improving Adversarial Robust Fairness via Anti-Bias Soft Label DistillationCode0
Poisoning Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks0
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models0
MIMIR: Masked Image Modeling for Mutual Information-based Adversarial RobustnessCode0
RoAST: Robustifying Language Models via Adversarial Perturbation with Selective TrainingCode0
Enhancing Robustness in Incremental Learning with Adversarial TrainingCode0
Indirect Gradient Matching for Adversarial Robust Distillation0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System0
ScAR: Scaling Adversarial Robustness for LiDAR Object DetectionCode0
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More0
Singular Regularization with Information Bottleneck Improves Model's Adversarial Robustness0
Exploring Adversarial Robustness of LiDAR-Camera Fusion Model in Autonomous Driving0
Adversarial Robust Memory-Based Continual LearnerCode0
Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses0
On the Adversarial Robustness of Graph Contrastive Learning Methods0
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence0
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMsCode1
Mixing Classifiers to Alleviate the Accuracy-Robustness Trade-Off0
Robust Graph Neural Networks via Unbiased AggregationCode0
Training robust and generalizable quantum modelsCode0
Adversarial Prompt Tuning for Vision-Language ModelsCode1
Towards Robust and Accurate Visual Prompting0
Adversarially Robust Spiking Neural Networks Through ConversionCode0
Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Data0
Measuring Adversarial Datasets0
Deep anytime-valid hypothesis testingCode0
Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness0
Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical FlowCode0
Multi-scale Diffusion Denoised SmoothingCode1
Deceptive Fairness Attacks on Graphs via Meta LearningCode0
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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