SOTAVerified

Adversarial Robustness

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

Papers

Showing 851900 of 1746 papers

TitleStatusHype
Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and DefenseCode1
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices0
Bispectral Neural NetworksCode1
Adversarial Robustness for Tabular Data through Cost and Utility Awareness0
FuncFooler: A Practical Black-box Attack Against Learning-based Binary Code Similarity Detection Methods0
Multi-Scale Architectures Matter: On the Adversarial Robustness of Flow-based Lossless Compression0
Robust Prototypical Few-Shot Organ Segmentation with Regularized Neural-ODEsCode0
GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks0
Lower Difficulty and Better Robustness: A Bregman Divergence Perspective for Adversarial Training0
Shortcut Learning of Large Language Models in Natural Language Understanding0
Different Spectral Representations in Optimized Artificial Neural Networks and BrainsCode0
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning0
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function PerspectiveCode1
Exploring Adversarial Robustness of Vision Transformers in the Spectral PerspectiveCode0
On the Privacy Effect of Data Enhancement via the Lens of MemorizationCode0
Two Heads are Better than One: Robust Learning Meets Multi-branch ModelsCode0
MENLI: Robust Evaluation Metrics from Natural Language InferenceCode1
Self-Knowledge Distillation via Dropout0
Adversarial robustness of VAEs through the lens of local geometryCode0
On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks0
Adversarial Robustness of MR Image Reconstruction under Realistic PerturbationsCode0
Understanding Adversarial Robustness of Vision Transformers via Cauchy ProblemCode0
Is current research on adversarial robustness addressing the right problem?0
Pro-tuning: Unified Prompt Tuning for Vision Tasks0
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks0
Visual correspondence-based explanations improve AI robustness and human-AI team accuracyCode1
Improving Adversarial Robustness via Mutual Information EstimationCode1
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial RobustnessCode1
Contrastive Self-Supervised Learning Leads to Higher Adversarial Susceptibility0
Do Perceptually Aligned Gradients Imply Adversarial Robustness?Code0
AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance0
One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training0
Careful What You Wish For: on the Extraction of Adversarially Trained ModelsCode0
Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks0
Tailoring Self-Supervision for Supervised LearningCode1
Assaying Out-Of-Distribution Generalization in Transfer LearningCode0
Adversarial Contrastive Learning via Asymmetric InfoNCECode1
Automated Repair of Neural NetworksCode0
CARBEN: Composite Adversarial Robustness BenchmarkCode1
Distance Learner: Incorporating Manifold Prior to Model TrainingCode1
Adversarially-Aware Robust Object DetectorCode1
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures0
Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual InformationCode0
Certified Adversarial Robustness via Anisotropic Randomized Smoothing0
Adversarial Robustness Assessment of NeuroEvolution Approaches0
RUSH: Robust Contrastive Learning via Randomized Smoothing0
Dynamic Time Warping based Adversarial Framework for Time-Series DomainCode0
How many perturbations break this model? Evaluating robustness beyond adversarial accuracyCode0
On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network0
Adversarial Robustness of Visual Dialog0
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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