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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 1201–1250 of 1746 papers

TitleStatusHype
Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging—0
GARNET: A Spectral Approach to Robust and Scalable Graph Neural Networks—0
General Coded Computing: Adversarial Settings—0
Generalizability of Adversarial Robustness Under Distribution Shifts—0
Generalizable Deepfake Detection with Phase-Based Motion Analysis—0
Generalization Certificates for Adversarially Robust Bayesian Linear Regression—0
Generalization Error Analysis of Neural networks with Gradient Based Regularization—0
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness—0
Incorporating Hidden Layer representation into Adversarial Attacks and Defences—0
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness—0
Generalizing and Improving Jacobian and Hessian Regularization—0
Generate and Verify: Semantically Meaningful Formal Analysis of Neural Network Perception Systems—0
Generating Structured Adversarial Attacks Using Frank-Wolfe Method—0
GenFighter: A Generative and Evolutive Textual Attack Removal—0
GenLabel: Mixup Relabeling using Generative Models—0
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing—0
GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks—0
Global Adversarial Robustness Guarantees for Neural Networks—0
GNN-Ensemble: Towards Random Decision Graph Neural Networks—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization—0
GridMix: Strong regularization through local context mapping—0
Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending against Poisoning Attacks—0
Guess First to Enable Better Compression and Adversarial Robustness—0
Guidance Through Surrogate: Towards a Generic Diagnostic Attack—0
Guided Interpolation for Adversarial Training—0
Harmonizing Feature Maps: A Graph Convolutional Approach for Enhancing Adversarial Robustness—0
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning—0
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework—0
Hierarchical Binding in Convolutional Neural Networks Confers Adversarial Robustness—0
Hierarchical Contextual Manifold Alignment for Structuring Latent Representations in Large Language Models—0
Hierarchical Verification for Adversarial Robustness—0
Holistic Adversarially Robust Pruning—0
Holistic Adversarial Robustness of Deep Learning Models—0
Homophily-Driven Sanitation View for Robust Graph Contrastive Learning—0
On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks—0
How and When Adversarial Robustness Transfers in Knowledge Distillation?—0
How benign is benign overfitting?—0
How Benign is Benign Overfitting ?—0
How Do Diffusion Models Improve Adversarial Robustness?—0
How do SGD hyperparameters in natural training affect adversarial robustness?—0
Towards Adversarially Robust Recommendation from Adaptive Fraudster Detection—0
How Robust are Randomized Smoothing based Defenses to Data Poisoning?—0
How to beat a Bayesian adversary—0
How to Enhance Downstream Adversarial Robustness (almost) without Touching the Pre-Trained Foundation Model?—0
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding—0
Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat Analysis—0
Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models—0
Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Models—0
Hyperbolic Contrastive 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