SOTAVerified

Adversarial Robustness

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

Papers

Showing 801850 of 1746 papers

TitleStatusHype
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge DistillationCode0
Fixed Inter-Neuron Covariability Induces Adversarial Robustness0
Exploring the Physical World Adversarial Robustness of Vehicle Detection0
Unsupervised Adversarial Detection without Extra Model: Training Loss Should ChangeCode0
RobustMQ: Benchmarking Robustness of Quantized Models0
Robust Linear Regression: Phase-Transitions and Precise Tradeoffs for General Norms0
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image ClassifiersCode0
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness0
Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning0
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives0
Characterizing Data Point Vulnerability via Average-Case RobustnessCode0
Exploring the Sharpened Cosine Similarity0
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix0
Homophily-Driven Sanitation View for Robust Graph Contrastive Learning0
A Holistic Assessment of the Reliability of Machine Learning Systems0
Omnipotent Adversarial Training in the WildCode0
Min-Max Optimization under Delays0
Function-Space Regularization for Deep Bayesian Classification0
A unifying framework for differentially private quantum algorithms0
A Theoretical Perspective on Subnetwork Contributions to Adversarial Robustness0
Kernels, Data & Physics0
On the Adversarial Robustness of Generative Autoencoders in the Latent Space0
Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness0
The Importance of Robust Features in Mitigating Catastrophic Forgetting0
Advancing Adversarial Training by Injecting Booster Signal0
Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning0
A Survey on Out-of-Distribution Evaluation of Neural NLP Models0
Computational Asymmetries in Robust ClassificationCode0
On Evaluating the Adversarial Robustness of Semantic Segmentation Models0
A Spectral Perspective towards Understanding and Improving Adversarial Robustness0
Enhancing Adversarial Training via Reweighting Optimization TrajectoryCode0
Adversarial Robustness Certification for Bayesian Neural NetworksCode0
Towards quantum enhanced adversarial robustness in machine learning0
Anticipatory Thinking Challenges in Open Worlds: Risk Management0
Physics-constrained Attack against Convolution-based Human Motion PredictionCode0
A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking0
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models0
Eight challenges in developing theory of intelligence0
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic ProgrammingCode0
Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language UnderstandingCode0
Revisiting and Advancing Adversarial Training Through A Simple Baseline0
AROID: Improving Adversarial Robustness Through Online Instance-Wise Data AugmentationCode0
Boosting Adversarial Robustness using Feature Level Stochastic SmoothingCode0
Expanding Scope: Adapting English Adversarial Attacks to ChineseCode0
Faithful Knowledge Distillation0
Transferable Adversarial Robustness for Categorical Data via Universal Robust EmbeddingsCode0
Revisiting the Trade-off between Accuracy and Robustness via Weight Distribution of FiltersCode0
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception0
Evaluating robustness of support vector machines with the Lagrangian dual approach0
A Closer Look at the Adversarial Robustness of Deep Equilibrium ModelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeBERTa (single model)Accuracy0.61Unverified
2ALBERT (single model)Accuracy0.59Unverified
3T5 (single model)Accuracy0.57Unverified
4SMART_RoBERTa (single model)Accuracy0.54Unverified
5FreeLB (single model)Accuracy0.5Unverified
6RoBERTa (single model)Accuracy0.5Unverified
7InfoBERT (single model)Accuracy0.46Unverified
8ELECTRA (single model)Accuracy0.42Unverified
9BERT (single model)Accuracy0.34Unverified
10SMART_BERT (single model)Accuracy0.3Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed classifierAccuracy95.23Unverified
2Stochastic-LWTA/PGD/WideResNet-34-10Accuracy92.26Unverified
3Stochastic-LWTA/PGD/WideResNet-34-5Accuracy91.88Unverified
4GLOT-DRAccuracy84.13Unverified
5TRADES-ANCRA/ResNet18Accuracy81.7Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (SGD, Cosine)Accuracy77.4Unverified
2ResNet-50 (SGD, Step)Accuracy76.9Unverified
3DeiT-S (AdamW, Cosine)Accuracy76.8Unverified
4ResNet-50 (AdamW, Cosine)Accuracy76.4Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy12.2Unverified
2ResNet-50 (SGD, Cosine)Accuracy3.3Unverified
3ResNet-50 (SGD, Step)Accuracy3.2Unverified
4ResNet-50 (AdamW, Cosine)Accuracy3.1Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (AdamW, Cosine)mean Corruption Error (mCE)59.3Unverified
2ResNet-50 (SGD, Step)mean Corruption Error (mCE)57.9Unverified
3ResNet-50 (SGD, Cosine)mean Corruption Error (mCE)56.9Unverified
4DeiT-S (AdamW, Cosine)mean Corruption Error (mCE)48Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy13Unverified
2ResNet-50 (SGD, Cosine)Accuracy8.4Unverified
3ResNet-50 (SGD, Step)Accuracy8.3Unverified
4ResNet-50 (AdamW, Cosine)Accuracy8.1Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed ClassifierClean Accuracy85.21Unverified
2ResNet18/MART-ANCRAClean Accuracy60.1Unverified