SOTAVerified

Adversarial Robustness

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

Papers

Showing 15511600 of 1746 papers

TitleStatusHype
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification0
Towards Sharper First-Order Adversary with Quantized GradientsCode1
Guess First to Enable Better Compression and Adversarial Robustness0
RECAST: Interactive Auditing of Automatic Toxicity Detection Models0
Adversarial Robustness Against the Union of Multiple Threat ModelsCode1
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability0
Optimal Statistical Guaratees for Adversarially Robust Gaussian Classification0
Adversarial Robustness via Runtime Masking and Cleansing0
Smooth Kernels Improve Adversarial Robustness and Perceptually-Aligned Gradients0
Optimising Neural Network Architectures for Provable Adversarial Robustness0
Adversarially Robust Neural Networks via Optimal Control: Bridging Robustness with Lyapunov Stability0
Benchmarking Adversarial Robustness0
Explainability and Adversarial Robustness for RNNsCode1
P-CapsNets: a General Form of Convolutional Neural Networks0
APRICOT: A Dataset of Physical Adversarial Attacks on Object DetectionCode0
What it Thinks is Important is Important: Robustness Transfers through Input GradientsCode0
Feature Losses for Adversarial Robustness0
Exploring the Back Alleys: Analysing The Robustness of Alternative Neural Network Architectures against Adversarial Attacks0
An Empirical Study on the Relation between Network Interpretability and Adversarial RobustnessCode0
Towards Robust Image Classification Using Sequential Attention Models0
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural NetworksCode0
Can Attention Masks Improve Adversarial Robustness?0
An Adaptive View of Adversarial Robustness from Test-time Smoothing DefenseCode0
Playing it Safe: Adversarial Robustness with an Abstain Option0
CAMUS: A Framework to Build Formal Specifications for Deep Perception Systems Using Simulators0
Universal Adversarial Robustness of Texture and Shape-Biased ModelsCode1
Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud ProcessingCode0
Adversarial Robustness of Flow-Based Generative Models0
AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate GradientsCode0
Finding a human-like classifier0
MadNet: Using a MAD Optimization for Defending Against Adversarial Attacks0
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional NetworksCode0
Fault Tolerance of Neural Networks in Adversarial Settings0
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications0
Certified Adversarial Robustness for Deep Reinforcement Learning0
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?0
A Useful Taxonomy for Adversarial Robustness of Neural Networks0
Are Perceptually-Aligned Gradients a General Property of Robust Classifiers?0
An empirical study of pretrained representations for few-shot classification0
Adversarial Robustness vs. Model Compression, or Both?Code0
Role of Spatial Context in Adversarial Robustness for Object DetectionCode0
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks0
Lower Bounds on Adversarial Robustness from Optimal TransportCode0
Visual Interpretability Alone Helps Adversarial Robustness0
Global Adversarial Robustness Guarantees for Neural Networks0
_1 Adversarial Robustness Certificates: a Randomized Smoothing Approach0
Towards Disentangling Non-Robust and Robust Components in Performance Metric0
SPROUT: Self-Progressing Robust Training0
Power up! Robust Graph Convolutional Network based on Graph Powering0
Defending Against Adversarial Examples by Regularized Deep Embedding0
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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