SOTAVerified

Adversarial Robustness

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

Papers

Showing 12011250 of 1746 papers

TitleStatusHype
Adversarial Robustness via Fisher-Rao RegularizationCode0
CARTL: Cooperative Adversarially-Robust Transfer LearningCode0
Corruption-Robust Offline Reinforcement Learning0
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training0
An Ensemble Approach Towards Adversarial Robustness0
Towards Defending against Adversarial Examples via Attack-Invariant Features0
Reliable Adversarial Distillation with Unreliable TeachersCode1
Towards the Memorization Effect of Neural Networks in Adversarial Training0
Adversarial Attack and Defense in Deep RankingCode1
RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models0
A Primer on Multi-Neuron Relaxation-based Adversarial Robustness Certification0
k-Mixup Regularization for Deep Learning via Optimal TransportCode0
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial RobustnessCode1
Improving Neural Network Robustness via Persistency of ExcitationCode0
PDPGD: Primal-Dual Proximal Gradient Descent Adversarial AttackCode0
Certified Robustness to Word Substitution Attack with Differential Privacy0
Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning0
Variational Autoencoders: A Harmonic Perspective0
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy LabelsCode0
Demotivate adversarial defense in remote sensing0
Robust Regularization with Adversarial Labelling of Perturbed Samples0
On Linear Stability of SGD and Input-Smoothness of Neural NetworksCode0
Adversarial Robustness against Multiple and Single l_p-Threat Models via Quick Fine-Tuning of Robust ClassifiersCode1
Deep Repulsive Prototypes for Adversarial Robustness0
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training0
Skew Orthogonal ConvolutionsCode1
Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams0
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural NetworksCode1
Adversarial examples attack based on random warm restart mechanism and improved Nesterov momentum0
Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks0
Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model0
A Finer Calibration Analysis for Adversarial Robustness0
On the Adversarial Robustness of Quantized Neural Networks0
Impact of Spatial Frequency Based Constraints on Adversarial Robustness0
Random Noise Defense Against Query-Based Black-Box AttacksCode1
Towards Adversarial Patch Analysis and Certified Defense against Crowd CountingCode0
Robust Certification for Laplace Learning on Geometric Graphs0
Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations0
Calibration and Consistency of Adversarial Surrogate Losses0
Removing Adversarial Noise in Class Activation Feature Space0
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?Code1
On the Sensitivity and Stability of Model Interpretations in NLPCode0
Does language help generalization in vision models?Code0
Orthogonalizing Convolutional Layers with the Cayley TransformCode1
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition0
Relating Adversarially Robust Generalization to Flat Minima0
Adversarial Robustness Guarantees for Gaussian ProcessesCode0
Universal Adversarial Training with Class-Wise Perturbations0
Adversarial Robustness under Long-Tailed DistributionCode1
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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