SOTAVerified

Adversarial Robustness

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

Papers

Showing 301350 of 1746 papers

TitleStatusHype
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial RobustnessCode1
Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspectiveCode1
Bridging Mode Connectivity in Loss Landscapes and Adversarial RobustnessCode1
Improving Adversarial Robustness via Promoting Ensemble DiversityCode1
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial RemovalCode1
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial RobustnessCode1
A Self-supervised Approach for Adversarial RobustnessCode1
Broken Neural Scaling LawsCode1
DF-RAP: A Robust Adversarial Perturbation for Defending against Deepfakes in Real-world Social Network ScenariosCode1
Renofeation: A Simple Transfer Learning Method for Improved Adversarial RobustnessCode1
Enhancing Adversarial Robustness via Score-Based OptimizationCode1
IQA-Adapter: Exploring Knowledge Transfer from Image Quality Assessment to Diffusion-based Generative ModelsCode1
Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM AssessmentCode1
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?Code1
Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented CommunicationCode1
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial RobustnessCode1
Generalized Real-World Super-Resolution through Adversarial RobustnessCode1
LyaNet: A Lyapunov Framework for Training Neural ODEsCode1
MNIST-C: A Robustness Benchmark for Computer VisionCode1
CARBEN: Composite Adversarial Robustness BenchmarkCode1
On the Adversarial Robustness of Vision TransformersCode1
Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and DefenseCode1
Multitask Learning Strengthens Adversarial RobustnessCode1
NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial PerturbationsCode1
Analyzing Adversarial Robustness of Deep Neural Networks in Pixel Space: a Semantic Perspective0
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks0
Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Data0
Constrained Learning with Non-Convex Losses0
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection0
A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models0
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness0
Adversarial Robustness Across Representation Spaces0
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs0
Constraining Logits by Bounded Function for Adversarial Robustness0
A More Biologically Plausible Local Learning Rule for ANNs0
A margin-based replacement for cross-entropy loss0
Adversarial Attacks and Defenses for Speech Recognition Systems0
ALMA: Aggregated Lipschitz Maximization Attack on Auto-encoders0
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation0
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks0
aiXamine: Simplified LLM Safety and Security0
AI-Compass: A Comprehensive and Effective Multi-module Testing Tool for AI Systems0
CARES: Comprehensive Evaluation of Safety and Adversarial Robustness in Medical LLMs0
Contextual Fusion For Adversarial Robustness0
A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models0
A Holistic Assessment of the Reliability of Machine Learning Systems0
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs0
A Closer Look at the Adversarial Robustness of Information Bottleneck Models0
A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks0
A Fundamental Accuracy--Robustness Trade-off in Regression and Classification0
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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