SOTAVerified

Adversarial Robustness

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

Papers

Showing 651700 of 1746 papers

TitleStatusHype
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
Sharpness-Aware Minimization Alone can Improve Adversarial RobustnessCode1
Stratified Adversarial Robustness with RejectionCode0
Revisiting Robustness in Graph Machine Learning0
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples0
Test-Time Adaptation with Perturbation Consistency Learning0
Lyapunov-Stable Deep Equilibrium Models0
Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks0
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
Using Z3 for Formal Modeling and Verification of FNN Global RobustnessCode0
Certified Adversarial Robustness Within Multiple Perturbation BoundsCode0
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
Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous DrivingCode1
Targeted Adversarial Attacks on Wind Power ForecastsCode0
Latent Feature Relation Consistency for Adversarial RobustnessCode0
Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness0
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
Towards Effective Adversarial Textured 3D Meshes on Physical Face RecognitionCode1
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm0
CAT:Collaborative Adversarial TrainingCode0
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution DetectionCode0
CFA: Class-wise Calibrated Fair Adversarial TrainingCode1
Verifying Properties of Tsetlin MachinesCode0
Feature Separation and Recalibration for Adversarial RobustnessCode1
Improved Adversarial Training Through Adaptive Instance-wise Loss SmoothingCode0
Enhancing Multiple Reliability Measures via Nuisance-extended Information BottleneckCode0
Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection0
Optimization and Optimizers for Adversarial Robustness0
Revisiting DeepFool: generalization and improvementCode0
Reliable and Efficient Evaluation of Adversarial Robustness for Deep Hashing-Based Retrieval0
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation0
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense0
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified RobustnessCode1
GNN-Ensemble: Towards Random Decision Graph Neural Networks0
TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and GeneralizationCode1
It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness0
Robust Mode Connectivity-Oriented Adversarial Defense: Enhancing Neural Network Robustness Against Diversified _p AttacksCode1
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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