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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 451–500 of 1746 papers

TitleStatusHype
Adversarial Masked Autoencoder Purifier with Defense Transferability—0
Adversarially Robust Neural Architectures—0
Ensemble Adversarial Defense via Integration of Multiple Dispersed Low Curvature Models—0
Chaos Theory and Adversarial Robustness—0
Characterizing the adversarial vulnerability of speech self-supervised learning—0
Erasing Concepts, Steering Generations: A Comprehensive Survey of Concept Suppression—0
Enhancing Adversarial Robustness in SNNs with Sparse Gradients—0
Class-Aware Domain Adaptation for Improving Adversarial Robustness—0
Class-Aware Robust Adversarial Training for Object Detection—0
Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information—0
A Framework for Verification of Wasserstein Adversarial Robustness—0
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning—0
Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning—0
Clustering Effect of Adversarial Robust Models—0
A Fundamental Accuracy--Robustness Trade-off in Regression and Classification—0
Adversarial Prompt Distillation for Vision-Language Models—0
Adversarially Robust Industrial Anomaly Detection Through Diffusion Model—0
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks—0
Enhancing Adversarial Robustness of Vision Language Models via Adversarial Mixture Prompt Tuning—0
Bridged Adversarial Training—0
AdPO: Enhancing the Adversarial Robustness of Large Vision-Language Models with Preference Optimization—0
Training Graph Neural Networks Using Non-Robust Samples—0
Boosting Certified Robustness for Time Series Classification with Efficient Self-Ensemble—0
A Closer Look at the Adversarial Robustness of Information Bottleneck Models—0
Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners—0
A Survey and Evaluation of Adversarial Attacks for Object Detection—0
Conflict-Aware Adversarial Training—0
Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI—0
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training—0
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness—0
aiXamine: Simplified LLM Safety and Security—0
Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons—0
SOAR: Second-Order Adversarial Regularization—0
Constrained Learning with Non-Convex Losses—0
Constraining Logits by Bounded Function for Adversarial Robustness—0
Contextual Fusion For Adversarial Robustness—0
Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization—0
Adversarially Robust Estimate and Risk Analysis in Linear Regression—0
Boosting Adversarial Robustness and Generalization with Structural Prior—0
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models—0
Cross-Entropy Loss Functions: Theoretical Analysis and Applications—0
CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness—0
Curvature Dynamic Black-box Attack: revisiting adversarial robustness via dynamic curvature estimation—0
A More Biologically Plausible Local Learning Rule for ANNs—0
DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation—0
Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustness—0
Boosting Accuracy and Robustness of Student Models via Adaptive Adversarial Distillation—0
Adversarial Robustness Across Representation Spaces—0
Adversarial Robustness through Local Linearization—0
Adversarial Robustness through Dynamic Ensemble Learning—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