SOTAVerified

Adversarial Robustness

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

Papers

Showing 10011050 of 1746 papers

TitleStatusHype
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness0
D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles0
White-Box Attacks on Hate-speech BERT Classifiers in German with Explicit and Implicit Character Level DefenseCode1
Deadwooding: Robust Global Pruning for Deep Neural Networks0
Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic PerturbationsCode1
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial AttacksCode0
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse TrainingCode1
LyaNet: A Lyapunov Framework for Training Neural ODEsCode1
Optimized Potential Initialization for Low-latency Spiking Neural Networks0
Finding Biological Plausibility for Adversarially Robust Features via Metameric TasksCode0
Smoothed Embeddings for Certified Few-Shot LearningCode0
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?Code1
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons0
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural NetworksCode1
Improving Robustness by Enhancing Weak SubnetsCode0
The Many Faces of Adversarial Risk0
Unveiling Project-Specific Bias in Neural Code Models0
Adversarial vulnerability of powerful near out-of-distribution detectionCode1
AugLy: Data Augmentations for RobustnessCode5
Tools and Practices for Responsible AI Engineering0
On Adversarial Robustness of Trajectory Prediction for Autonomous VehiclesCode1
Towards Adversarially Robust Deep Image Denoising0
GenLabel: Mixup Relabeling using Generative Models0
On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous DrivingCode1
Rethinking Feature Uncertainty in Stochastic Neural Networks for Adversarial Robustness0
Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers0
Associative Adversarial Learning Based on Selective Attack0
Perlin Noise Improve Adversarial Robustness0
Understanding and Measuring Robustness of Multimodal Learning0
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?Code1
On the Adversarial Robustness of Causal Algorithmic RecourseCode0
Improving Robustness with Image Filtering0
Certified Federated Adversarial Training0
Analyzing and Improving the Robustness of Tabular Classifiers using Counterfactual ExplanationsCode0
The King is Naked: on the Notion of Robustness for Natural Language ProcessingCode0
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer0
On Adversarial Robustness of Point Cloud Semantic SegmentationCode0
Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach0
PixMix: Dreamlike Pictures Comprehensively Improve Safety MeasuresCode1
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch DetectionCode1
SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization0
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial RemovalCode1
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial RobustnessCode1
On the Existence of the Adversarial Bayes Classifier (Extended Version)0
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?Code1
Is Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks?0
A Unified Framework for Adversarial Attack and Defense in Constrained Feature SpaceCode1
Training Efficiency and Robustness in Deep LearningCode1
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems0
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines0
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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