SOTAVerified

Adversarial Robustness

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

Papers

Showing 851900 of 1746 papers

TitleStatusHype
A Closer Look at the Adversarial Robustness of Deep Equilibrium ModelsCode0
Robust low-rank training via approximate orthonormal constraints0
Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review0
Exploiting Explainability to Design Adversarial Attacks and Evaluate Attack Resilience in Hate-Speech Detection Models0
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study0
Two Heads are Better than One: Towards Better Adversarial Robustness by Combining Transduction and Rejection0
On the Importance of Backbone to the Adversarial Robustness of Object DetectorsCode0
Carefully Blending Adversarial Training, Purification, and Aggregation Improves Adversarial RobustnessCode0
IDEA: Invariant Defense for Graph Adversarial Robustness0
Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text0
AdvFunMatch: When Consistent Teaching Meets Adversarial Robustness0
Non-adversarial Robustness of Deep Learning Methods for Computer Vision0
Adversarial robustness of amortized Bayesian inferenceCode0
Expressive Losses for Verified Robustness via Convex CombinationsCode0
DeepBern-Nets: Taming the Complexity of Certifying Neural Networks using Bernstein Polynomial Activations and Precise Bound PropagationCode0
Annealing Self-Distillation Rectification Improves Adversarial TrainingCode0
Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend0
Raising the Bar for Certified Adversarial Robustness with Diffusion Models0
Variational ClassificationCode0
Iterative Adversarial Attack on Image-guided Story Ending Generation0
Releasing Inequality Phenomena in L_-Adversarial Training via Input Gradient Distillation0
Stochastic Security as a Performance Metric for Quantum-enhanced Generative AI0
Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications0
Inter-frame Accelerate Attack against Video Interpolation Models0
Randomized Smoothing with Masked Inference for Adversarially Robust Text ClassificationsCode0
Investigating the Corruption Robustness of Image Classifiers with Random Lp-norm CorruptionsCode0
Stratified Adversarial Robustness with RejectionCode0
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples0
Revisiting Robustness in Graph Machine Learning0
Test-Time Adaptation with Perturbation Consistency Learning0
Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks0
Lyapunov-Stable Deep Equilibrium Models0
Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning0
Evaluating Adversarial Robustness on Document Image Classification0
Robust and differentially private stochastic linear bandits0
Individual Fairness in Bayesian Neural NetworksCode0
Certified Adversarial Robustness Within Multiple Perturbation BoundsCode0
Using Z3 for Formal Modeling and Verification of FNN Global RobustnessCode0
GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative ModelsCode0
Wavelets Beat Monkeys at Adversarial Robustness0
Cross-Entropy Loss Functions: Theoretical Analysis and Applications0
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection0
Hyper-parameter Tuning for Adversarially Robust ModelsCode0
CGDTest: A Constrained Gradient Descent Algorithm for Testing Neural Networks0
Towards Adversarially Robust Continual Learning0
Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial RobustnessCode0
Latent Feature Relation Consistency for Adversarial RobustnessCode0
Targeted Adversarial Attacks on Wind Power ForecastsCode0
Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness0
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm0
Show:102550
← PrevPage 18 of 35Next →

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