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Adversarial Robustness

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

Papers

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Showing 1451–1500 of 1746 papers

TitleStatusHype
Adversarial Robustness of Supervised Sparse CodingCode0
On the Adversarial Robustness of LASSO Based Feature Selection—0
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness—0
A case for new neural networks smoothness constraints—0
FADER: Fast Adversarial Example Rejection—0
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
The Intrinsic Dimension of Images and Its Impact on Learning—0
Quantifying Adversarial Sensitivity of a Model as a Function of the Image Distribution—0
Affine-Invariant Robust Training—0
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination—0
Improve Adversarial Robustness via Weight Penalization on Classification Layer—0
Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature PerspectiveCode0
Constraining Logits by Bounded Function for Adversarial Robustness—0
Do Wider Neural Networks Really Help Adversarial Robustness?—0
Query complexity of adversarial attacks—0
On The Adversarial Robustness of 3D Point Cloud Classification—0
Proper Measure for Adversarial Robustness—0
Imbalanced Gradients: A New Cause of Overestimated Adversarial Robustness—0
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated GradientsCode0
Differentially Private Adversarial Robustness Through Randomized Perturbations—0
Semantics-Preserving Adversarial Training—0
Adversarial robustness via stochastic regularization of neural activation sensitivity—0
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial RobustnessCode0
Feature Distillation With Guided Adversarial Contrastive Learning—0
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning—0
Label Smoothing and Adversarial Robustness—0
On the Transferability of Minimal Prediction Preserving Inputs in Question Answering—0
Large Norms of CNN Layers Do Not Hurt Adversarial RobustnessCode0
Robust Deep Learning Ensemble against Deception—0
Achieving Adversarial Robustness via Sparsity—0
Defending Against Multiple and Unforeseen Adversarial Videos—0
Second Order Optimization for Adversarial Robustness and Interpretability—0
End-to-end Kernel Learning via Generative Random Fourier FeaturesCode0
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks—0
Adversarially Robust Neural Architectures—0
Perceptual Deep Neural Networks: Adversarial Robustness through Input Recreation—0
Rethinking Non-idealities in Memristive Crossbars for Adversarial Robustness in Neural Networks—0
Likelihood Landscapes: A Unifying Principle Behind Many Adversarial Defenses—0
Towards adversarial robustness with 01 loss neural networksCode0
Improving adversarial robustness of deep neural networks by using semantic information—0
A Deep Dive into Adversarial Robustness in Zero-Shot LearningCode0
Adversarial Concurrent Training: Optimizing Robustness and Accuracy Trade-off of Deep Neural NetworksCode0
On the Generalization Properties of Adversarial Training—0
Adversary Agnostic Robust Deep Reinforcement Learning—0
Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness—0
Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations—0
TREND: Transferability based Robust ENsemble DesignCode0
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data—0
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All datasetsAdvGLUECIFAR-10ImageNetImageNet-AImageNet-CStylized ImageNetCIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeBERTa (single model)Accuracy0.61—Unverified
2ALBERT (single model)Accuracy0.59—Unverified
3T5 (single model)Accuracy0.57—Unverified
4SMART_RoBERTa (single model)Accuracy0.54—Unverified
5FreeLB (single model)Accuracy0.5—Unverified
6RoBERTa (single model)Accuracy0.5—Unverified
7InfoBERT (single model)Accuracy0.46—Unverified
8ELECTRA (single model)Accuracy0.42—Unverified
9BERT (single model)Accuracy0.34—Unverified
10SMART_BERT (single model)Accuracy0.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed classifierAccuracy95.23—Unverified
2Stochastic-LWTA/PGD/WideResNet-34-10Accuracy92.26—Unverified
3Stochastic-LWTA/PGD/WideResNet-34-5Accuracy91.88—Unverified
4GLOT-DRAccuracy84.13—Unverified
5TRADES-ANCRA/ResNet18Accuracy81.7—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (SGD, Cosine)Accuracy77.4—Unverified
2ResNet-50 (SGD, Step)Accuracy76.9—Unverified
3DeiT-S (AdamW, Cosine)Accuracy76.8—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy76.4—Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy12.2—Unverified
2ResNet-50 (SGD, Cosine)Accuracy3.3—Unverified
3ResNet-50 (SGD, Step)Accuracy3.2—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy3.1—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (AdamW, Cosine)mean Corruption Error (mCE)59.3—Unverified
2ResNet-50 (SGD, Step)mean Corruption Error (mCE)57.9—Unverified
3ResNet-50 (SGD, Cosine)mean Corruption Error (mCE)56.9—Unverified
4DeiT-S (AdamW, Cosine)mean Corruption Error (mCE)48—Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy13—Unverified
2ResNet-50 (SGD, Cosine)Accuracy8.4—Unverified
3ResNet-50 (SGD, Step)Accuracy8.3—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy8.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed ClassifierClean Accuracy85.21—Unverified
2ResNet18/MART-ANCRAClean Accuracy60.1—Unverified