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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 651–700 of 1746 papers

TitleStatusHype
Evaluation Methodology for Attacks Against Confidence Thresholding Models—0
A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing—0
Evolutionary Reinforcement Learning: A Systematic Review and Future Directions—0
A Theoretical Perspective on Subnetwork Contributions to Adversarial Robustness—0
GridMix: Strong regularization through local context mapping—0
Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense—0
Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification—0
ATRAS: Adversarially Trained Robust Architecture Search—0
Experimental robustness benchmark of quantum neural network on a superconducting quantum processor—0
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks—0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System—0
Attacking Graph Classification via Bayesian Optimisation—0
Enhancing Quantum Adversarial Robustness by Randomized Encodings—0
A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning—0
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness—0
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness—0
Adversarial Robustness is at Odds with Lazy Training—0
Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection—0
Generalizing and Improving Jacobian and Hessian Regularization—0
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring—0
Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review—0
Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers—0
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness—0
Adversarial Examples are Misaligned in Diffusion Model Manifolds—0
Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods—0
Exploring Adversarial Robustness of LiDAR-Camera Fusion Model in Autonomous Driving—0
Generate and Verify: Semantically Meaningful Formal Analysis of Neural Network Perception Systems—0
Exploring Adversarial Transferability between Kolmogorov-arnold Networks—0
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training—0
ASAT: Adaptively Scaled Adversarial Training in Time Series—0
Enhancing Adversarial Robustness of Vision Language Models via Adversarial Mixture Prompt Tuning—0
Exploring Layerwise Adversarial Robustness Through the Lens of t-SNE—0
Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning—0
Exploring Robust Features for Improving Adversarial Robustness—0
Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume—0
Exploring the Adversarial Robustness of CLIP for AI-generated Image Detection—0
Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees—0
Exploring the Hyperparameter Landscape of Adversarial Robustness—0
Exploring the Physical World Adversarial Robustness of Vehicle Detection—0
Exploring the Sharpened Cosine Similarity—0
Enhancing Adversarial Robustness in SNNs with Sparse Gradients—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
How Robust are Randomized Smoothing based Defenses to Data Poisoning?—0
Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection—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
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness—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