SOTAVerified

Adversarial Robustness

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

Papers

Showing 13511400 of 1746 papers

TitleStatusHype
On 1/n neural representation and robustnessCode0
Overcomplete Representations Against Adversarial VideosCode0
Using Feature Alignment Can Improve Clean Average Precision and Adversarial Robustness in Object DetectionCode1
Evaluating adversarial robustness in simulated cerebellum0
Unsupervised Adversarially-Robust Representation Learning on Graphs0
FAT: Federated Adversarial Training0
Interpretable Graph Capsule Networks for Object Recognition0
Stochastic Gradient Descent with Nonlinear Conjugate Gradient-Style Adaptive Momentum0
How Robust are Randomized Smoothing based Defenses to Data Poisoning?0
Adversarial Robustness Across Representation Spaces0
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary SeminormsCode0
On the Trade-off between Adversarial and Backdoor RobustnessCode1
Robust and Private Learning of Halfspaces0
Architectural Adversarial Robustness: The Case for Deep Pursuit0
Incorporating Hidden Layer representation into Adversarial Attacks and Defences0
A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks0
Regularization with Latent Space Virtual Adversarial TrainingCode1
Exposing the Robustness and Vulnerability of Hybrid 8T-6T SRAM Memory Architectures to Adversarial Attacks in Deep Neural Networks0
aw_nas: A Modularized and Extensible NAS framework0
On Adversarial Robustness of 3D Point Cloud Classification under Adaptive Attacks0
A More Biologically Plausible Local Learning Rule for ANNs0
A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated LearningCode1
Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi DiagramsCode0
Effective, Efficient and Robust Neural Architecture Search0
An Experimental Study of Semantic Continuity for Deep Learning Models0
Contextual Fusion For Adversarial Robustness0
Robustified Domain Adaptation0
SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert PatcherCode1
Probing Predictions on OOD Images via Nearest CategoriesCode0
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks0
Adversarial Image Color Transformations in Explicit Color Filter SpaceCode1
Bridging the Performance Gap between FGSM and PGD Adversarial TrainingCode0
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks0
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification0
Robust Pre-Training by Adversarial Contrastive LearningCode1
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy0
Towards Robust Neural Networks via Orthogonal DiversityCode0
Adversarial Robustness of Supervised Sparse CodingCode0
On the Adversarial Robustness of LASSO Based Feature Selection0
A case for new neural networks smoothness constraints0
RobustBench: a standardized adversarial robustness benchmarkCode1
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness0
FADER: Fast Adversarial Example Rejection0
Weight-Covariance Alignment for Adversarially Robust Neural NetworksCode0
An Analysis of Robustness of Non-Lipschitz NetworksCode0
FaiR-N: Fair and Robust Neural Networks for Structured DataCode0
Shape-Texture Debiased Neural Network TrainingCode1
The Intrinsic Dimension of Images and Its Impact on Learning0
Quantifying Adversarial Sensitivity of a Model as a Function of the Image Distribution0
Improve Adversarial Robustness via Weight Penalization on Classification Layer0
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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