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Adversarial Robustness

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1101–1150 of 1746 papers

TitleStatusHype
On Adversarial Robustness of Synthetic Code Generation—0
A Frequency Perspective of Adversarial Robustness—0
Towards Assurance of LLM Adversarial Robustness using Ontology-Driven Argumentation—0
On Connections between Regularizations for Improving DNN Robustness—0
A Framework for Verification of Wasserstein Adversarial Robustness—0
A Flat Minima Perspective on Understanding Augmentations and Model Robustness—0
A Finer Calibration Analysis for Adversarial Robustness—0
On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices—0
Affine-Invariant Robust Training—0
On Evaluating the Adversarial Robustness of Semantic Segmentation Models—0
Towards Assuring EU AI Act Compliance and Adversarial Robustness of LLMs—0
On existence, uniqueness and scalability of adversarial robustness measures for AI classifiers—0
On Extending the Automatic Test Markup Language (ATML) for Machine Learning—0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning—0
On Fragile Features and Batch Normalization in Adversarial Training—0
Contrastive Self-Supervised Learning Leads to Higher Adversarial Susceptibility—0
On Inherent Adversarial Robustness of Active Vision Systems—0
Adversarial Training via Adaptive Knowledge Amalgamation of an Ensemble of Teachers—0
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination—0
On Meaning-Preserving Adversarial Perturbations for Sequence-to-Sequence Models—0
On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex—0
On Saliency Maps and Adversarial Robustness—0
On sparse connectivity, adversarial robustness, and a novel model of the artificial neuron—0
On The Adversarial Robustness of 3D Point Cloud Classification—0
On Adversarial Robustness of 3D Point Cloud Classification under Adaptive Attacks—0
On the Adversarial Robustness of Benjamini Hochberg—0
Adversarial Training for Face Recognition Systems using Contrastive Adversarial Learning and Triplet Loss Fine-tuning—0
AdvDO: Realistic Adversarial Attacks for Trajectory Prediction—0
Challenging the adversarial robustness of DNNs based on error-correcting output codes—0
On the Adversarial Robustness of Generative Autoencoders in the Latent Space—0
On the Adversarial Robustness of Graph Contrastive Learning Methods—0
On the Adversarial Robustness of LASSO Based Feature Selection—0
On the Adversarial Robustness of Learning-based Image Compression Against Rate-Distortion Attacks—0
On the Adversarial Robustness of Mixture of Experts—0
Adversarial Test on Learnable Image Encryption—0
On the Adversarial Robustness of Multivariate Robust Estimation—0
On the Adversarial Robustness of Neural Networks without Weight Transport—0
On the Adversarial Robustness of Quantized Neural Networks—0
On the Adversarial Robustness of Subspace Learning—0
On the benefits of knowledge distillation for adversarial robustness—0
Stochastic Security as a Performance Metric for Quantum-enhanced Generative AI—0
Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness—0
Adversarial Robustness with Semi-Infinite Constrained Learning—0
On the Effectiveness of Low Frequency Perturbations—0
On the Effectiveness of Minimal Context Selection for Robust Question Answering—0
On the Effect of Low-Rank Weights on Adversarial Robustness of Neural Networks—0
On the Effect of Pruning on Adversarial Robustness—0
AI Safety in Practice: Enhancing Adversarial Robustness in Multimodal Image Captioning—0
On the Existence of The Adversarial Bayes Classifier—0
On the Existence of the Adversarial Bayes Classifier (Extended Version)—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