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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 701–750 of 1746 papers

TitleStatusHype
Towards Adversarially Robust Recommendation from Adaptive Fraudster Detection—0
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks—0
Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review—0
Fair Robust Active Learning by Joint Inconsistency—0
How Do Diffusion Models Improve Adversarial Robustness?—0
Faithful Knowledge Distillation—0
Adversarial Examples are Misaligned in Diffusion Model Manifolds—0
How do SGD hyperparameters in natural training affect adversarial robustness?—0
Fast Adversarial Training against Textual Adversarial Attacks—0
Adversarial Robustness of Program Synthesis Models—0
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training—0
ASAT: Adaptively Scaled Adversarial Training in Time Series—0
Adversarial Robustness of Similarity-Based Link Prediction—0
Enhancing Adversarial Robustness of Vision Language Models via Adversarial Mixture Prompt Tuning—0
Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning—0
Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees—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
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks—0
Enhancing Adversarial Robustness in SNNs with Sparse Gradients—0
Feature Losses for Adversarial Robustness—0
Adversarial Examples Are a Natural Consequence of Test Error in Noise—0
Adversarial Robustness in RGB-Skeleton Action Recognition: Leveraging Attention Modality Reweighter—0
Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection—0
How benign is benign overfitting?—0
How Benign is Benign Overfitting ?—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
How Robust are Randomized Smoothing based Defenses to Data Poisoning?—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
Finding a human-like classifier—0
Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Models—0
Adversarial Robustness in Parameter-Space Classifiers—0
Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons—0
Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering—0
Fixed Inter-Neuron Covariability Induces Adversarial Robustness—0
Homophily-Driven Sanitation View for Robust Graph Contrastive Learning—0
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification—0
Flooding-X: Improving BERT's Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning—0
A Robust Adversarial Ensemble with Causal (Feature Interaction) Interpretations for Image Classification—0
Empirical Study of the Decision Region and Robustness in Deep Neural Networks—0
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification—0
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations—0
Emoti-Attack: Zero-Perturbation Adversarial Attacks on NLP Systems via Emoji Sequences—0
_1 Adversarial Robustness Certificates: a Randomized Smoothing Approach—0
Frequency Regularization for Improving Adversarial Robustness—0
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains—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