SOTAVerified

Adversarial Robustness

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

Papers

Showing 11011150 of 1746 papers

TitleStatusHype
A Framework for Verification of Wasserstein Adversarial Robustness0
Are models trained on temporally-continuous data streams more adversarially robust?0
Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN0
Gated Information Bottleneck for Generalization in Sequential EnvironmentsCode0
Parameterizing Activation Functions for Adversarial Robustness0
Adversarial Training for Face Recognition Systems using Contrastive Adversarial Learning and Triplet Loss Fine-tuning0
Adversarial Robustness of Program Synthesis Models0
Observations on K-image Expansion of Image-Mixing Augmentation for ClassificationCode0
Explainability-Aware One Point Attack for Point Cloud Neural NetworksCode1
The Eigenlearning Framework: A Conservation Law Perspective on Kernel Regression and Wide Neural NetworksCode1
Exploring Architectural Ingredients of Adversarially Robust Deep Neural NetworksCode1
Improving Adversarial Robustness for Free with Snapshot Ensemble0
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNsCode1
Adversarial Robustness Verification and Attack Synthesis in Stochastic SystemsCode0
Does Adversarial Robustness Really Imply Backdoor Vulnerability?0
Certified Adversarial Robustness Under the Bounded Support Set0
Dissecting Local Properties of Adversarial Examples0
k-Mixup Regularization for Deep Learning via Optimal Transport0
Use of small auxiliary networks and scarce data to improve the adversarial robustness of deep learning models0
Biased Multi-Domain Adversarial Training0
Empirical Study of the Decision Region and Robustness in Deep Neural Networks0
Function-Space Variational Inference for Deep Bayesian Classification0
Provably Robust Transfer0
Delving into Feature Space: Improving Adversarial Robustness by Feature Spectral Regularization0
GARNET: A Spectral Approach to Robust and Scalable Graph Neural Networks0
Adversarial Robustness via Adaptive Label Smoothing0
Efficient Certification for Probabilistic Robustness0
Learning Sample Reweighting for Adversarial Robustness0
Towards Achieving Adversarial Robustness Beyond Perceptual Limits0
Resilience to Multiple Attacks via Adversarially Trained MIMO Ensembles0
An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness0
Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view InconsistencyCode0
CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural NetworksCode0
Robust Physical-World Attacks on Face Recognition0
Simple Post-Training Robustness Using Test Time Augmentations and Random ForestCode0
Adversarial Examples for Evaluating Math Word Problem SolversCode0
How to Select One Among All? An Extensive Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language UnderstandingCode1
Adversarial Bone Length Attack on Action Recognition0
RobustART: Benchmarking Robustness on Architecture Design and Training TechniquesCode1
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency0
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness0
Impact of Attention on Adversarial Robustness of Image Classification Models0
Adversarial Robustness for Unsupervised Domain Adaptation0
Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings0
Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks0
A Hierarchical Assessment of Adversarial SeverityCode0
Generalized Real-World Super-Resolution through Adversarial RobustnessCode1
Adversarially Robust One-class Novelty DetectionCode0
Bridged Adversarial Training0
Are socially-aware trajectory prediction models really socially-aware?Code1
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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