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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 1151–1200 of 1746 papers

TitleStatusHype
Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks—0
Wavelets Beat Monkeys at Adversarial Robustness—0
Fundamental Limits in Formal Verification of Message-Passing Neural Networks—0
What are effective labels for augmented data? Improving robustness with AutoLabel—0
When is dataset cartography ineffective? Using training dynamics does not improve robustness against Adversarial SQuAD—0
Who's Afraid of Thomas Bayes?—0
With Great Backbones Comes Great Adversarial Transferability—0
XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars—0
Your Classifier Can Do More: Towards Bridging the Gaps in Classification, Robustness, and Generation—0
Extreme Miscalibration and the Illusion of Adversarial Robustness—0
F^2AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns—0
Facial Attributes: Accuracy and Adversarial Robustness—0
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices—0
FADER: Fast Adversarial Example Rejection—0
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks—0
Fair Robust Active Learning by Joint Inconsistency—0
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training—0
Faithful Knowledge Distillation—0
Fast Adversarial Training against Textual Adversarial Attacks—0
Fast Adversarial Training with Weak-to-Strong Spatial-Temporal Consistency in the Frequency Domain on Videos—0
FAT: Federated Adversarial Training—0
Fault Tolerance of Neural Networks in Adversarial Settings—0
Feature Averaging: An Implicit Bias of Gradient Descent Leading to Non-Robustness in Neural Networks—0
Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness—0
Feature Distillation With Guided Adversarial Contrastive Learning—0
Feature Losses for Adversarial Robustness—0
Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness—0
FedProphet: Memory-Efficient Federated Adversarial Training via Theoretic-Robustness and Low-Inconsistency Cascade Learning—0
Feedback Learning for Improving the Robustness of Neural Networks—0
Fermi-Bose Machine achieves both generalization and adversarial robustness—0
Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
Finding a human-like classifier—0
Finding Dynamics Preserving Adversarial Winning Tickets—0
Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering—0
Fixed Inter-Neuron Covariability Induces Adversarial Robustness—0
Flooding-X: Improving BERT's Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning—0
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification—0
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations—0
Framework for Progressive Knowledge Fusion in Large Language Models Through Structured Conceptual Redundancy Analysis—0
Frequency Regularization for Improving Adversarial Robustness—0
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks—0
From Sound Representation to Model Robustness—0
FullLoRA-AT: Efficiently Boosting the Robustness of Pretrained Vision Transformers—0
Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time—0
FuncFooler: A Practical Black-box Attack Against Learning-based Binary Code Similarity Detection Methods—0
Functional Network: A Novel Framework for Interpretability of Deep Neural Networks—0
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions—0
Function-Space Regularization for Deep Bayesian Classification—0
Function-Space Variational Inference for Deep Bayesian Classification—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