SOTAVerified

Adversarial Robustness

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

Papers

Showing 11511200 of 1746 papers

TitleStatusHype
Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications0
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness0
AdvDrop: Adversarial Attack to DNNs by Dropping InformationCode1
ASAT: Adaptively Scaled Adversarial Training in Time Series0
Pruning in the Face of AdversariesCode0
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification0
Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student BetterCode1
Neural Architecture Dilation for Adversarial Robustness0
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric LearningCode1
On the Effect of Pruning on Adversarial Robustness0
Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100Code1
Robust Transfer Learning with Pretrained Language Models through Adapters0
AdvRush: Searching for Adversarially Robust Neural ArchitecturesCode1
Towards Adversarially Robust and Domain Generalizable Stereo Matching by Rethinking DNN Feature Backbones0
Who's Afraid of Thomas Bayes?0
Enhancing Adversarial Robustness via Test-time Transformation EnsemblingCode1
WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image ClassificationCode1
Clipped Hyperbolic Classifiers Are Super-Hyperbolic ClassifiersCode1
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks0
Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic GradientsCode1
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning0
A Closer Look at the Adversarial Robustness of Information Bottleneck Models0
Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems0
Improving Model Robustness with Latent Distribution Locally and GloballyCode0
Understanding Intrinsic Robustness Using Label UncertaintyCode0
GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization0
Generalization Error Analysis of Neural networks with Gradient Based Regularization0
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction0
Adversarial Robustness of Streaming Algorithms through Importance Sampling0
RAILS: A Robust Adversarial Immune-inspired Learning SystemCode1
Multi-stage Optimization based Adversarial Training0
On the (Un-)Avoidability of Adversarial Examples0
Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification TasksCode0
On Adversarial Robustness of Synthetic Code Generation0
NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations0
Policy Smoothing for Provably Robust Reinforcement Learning0
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Adversarial Robustness0
Attacking Graph Classification via Bayesian Optimisation0
Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-offCode1
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial ExamplesCode3
Residual Error: a New Performance Measure for Adversarial Robustness0
Less is More: Feature Selection for Adversarial Robustness with Compressive Counter-Adversarial Attacks0
Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated LearningCode1
Analyzing Adversarial Robustness of Deep Neural Networks in Pixel Space: a Semantic Perspective0
Adversarial Visual Robustness by Causal InterventionCode1
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation0
Towards Adversarial Robustness via Transductive Learning0
Probabilistic Margins for Instance Reweighting in Adversarial TrainingCode1
ATRAS: Adversarially Trained Robust Architecture Search0
Understanding the Interplay between Privacy and Robustness in Federated Learning0
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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