SOTAVerified

Adversarial Robustness

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 701–750 of 1746 papers

TitleStatusHype
A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (TRASHFIRE)Code0
Fast Adversarial Training against Textual Adversarial Attacks—0
CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications—0
The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness—0
Adversarial Robustness of Link Sign Prediction in Signed Graphs—0
Universally Robust Graph Neural Networks by Preserving Neighbor Similarity—0
Adapters Mixup: Mixing Parameter-Efficient Adapters to Enhance the Adversarial Robustness of Fine-tuned Pre-trained Text Classifiers—0
Robustness Against Adversarial Attacks via Learning Confined Adversarial Polytopes—0
Adversarial Examples are Misaligned in Diffusion Model Manifolds—0
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 Transformers—0
Random Entangled Tokens for Adversarially Robust Vision Transformer—0
CausalPC: Improving the Robustness of Point Cloud Classification by Causal Effect Identification—0
Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners—0
Towards Understanding and Improving Adversarial Robustness of Vision Transformers—0
Robust Distillation via Untargeted and Targeted Intermediate Adversarial Samples—0
Towards adversarial robustness verification of no-reference image-and video-quality metricsCode0
Robust Survival Analysis with Adversarial Regularization—0
ARBiBench: Benchmarking Adversarial Robustness of Binarized Neural Networks—0
LRS: Enhancing Adversarial Transferability through Lipschitz Regularized SurrogateCode0
Scaling Compute Is Not All You Need for Adversarial RobustnessCode0
The Pros and Cons of Adversarial Robustness—0
The Ultimate Combo: Boosting Adversarial Example Transferability by Composing Data AugmentationsCode0
Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off—0
Adversarial Robustness on Image Classification with k-means—0
Universal Adversarial Framework to Improve Adversarial Robustness for Diabetic Retinopathy Detection—0
ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement LearningCode0
Initialization Matters for Adversarial Transfer LearningCode0
Poisoning Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks—0
Improving Adversarial Robust Fairness via Anti-Bias Soft Label DistillationCode0
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models—0
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 Distillation—0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System—0
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More—0
ScAR: Scaling Adversarial Robustness for LiDAR Object DetectionCode0
Singular Regularization with Information Bottleneck Improves Model's Adversarial Robustness—0
Exploring Adversarial Robustness of LiDAR-Camera Fusion Model in Autonomous Driving—0
Adversarial Robust Memory-Based Continual LearnerCode0
Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses—0
On the Adversarial Robustness of Graph Contrastive Learning Methods—0
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence—0
Mixing Classifiers to Alleviate the Accuracy-Robustness Trade-Off—0
Robust Graph Neural Networks via Unbiased AggregationCode0
Training robust and generalizable quantum modelsCode0
Towards Robust and Accurate Visual Prompting—0
Adversarially Robust Spiking Neural Networks Through ConversionCode0
Show:102550
← PrevPage 15 of 35Next →
All datasetsAdvGLUECIFAR-10ImageNetImageNet-AImageNet-CStylized ImageNetCIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeBERTa (single model)Accuracy0.61—Unverified
2ALBERT (single model)Accuracy0.59—Unverified
3T5 (single model)Accuracy0.57—Unverified
4SMART_RoBERTa (single model)Accuracy0.54—Unverified
5FreeLB (single model)Accuracy0.5—Unverified
6RoBERTa (single model)Accuracy0.5—Unverified
7InfoBERT (single model)Accuracy0.46—Unverified
8ELECTRA (single model)Accuracy0.42—Unverified
9BERT (single model)Accuracy0.34—Unverified
10SMART_BERT (single model)Accuracy0.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed classifierAccuracy95.23—Unverified
2Stochastic-LWTA/PGD/WideResNet-34-10Accuracy92.26—Unverified
3Stochastic-LWTA/PGD/WideResNet-34-5Accuracy91.88—Unverified
4GLOT-DRAccuracy84.13—Unverified
5TRADES-ANCRA/ResNet18Accuracy81.7—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (SGD, Cosine)Accuracy77.4—Unverified
2ResNet-50 (SGD, Step)Accuracy76.9—Unverified
3DeiT-S (AdamW, Cosine)Accuracy76.8—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy76.4—Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy12.2—Unverified
2ResNet-50 (SGD, Cosine)Accuracy3.3—Unverified
3ResNet-50 (SGD, Step)Accuracy3.2—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy3.1—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (AdamW, Cosine)mean Corruption Error (mCE)59.3—Unverified
2ResNet-50 (SGD, Step)mean Corruption Error (mCE)57.9—Unverified
3ResNet-50 (SGD, Cosine)mean Corruption Error (mCE)56.9—Unverified
4DeiT-S (AdamW, Cosine)mean Corruption Error (mCE)48—Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy13—Unverified
2ResNet-50 (SGD, Cosine)Accuracy8.4—Unverified
3ResNet-50 (SGD, Step)Accuracy8.3—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy8.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed ClassifierClean Accuracy85.21—Unverified
2ResNet18/MART-ANCRAClean Accuracy60.1—Unverified