SOTAVerified

Adversarial Robustness

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 801–850 of 1746 papers

TitleStatusHype
A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models—0
Curvature Dynamic Black-box Attack: revisiting adversarial robustness via dynamic curvature estimation—0
A More Biologically Plausible Local Learning Rule for ANNs—0
CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness—0
Cross-Entropy Loss Functions: Theoretical Analysis and Applications—0
A margin-based replacement for cross-entropy loss—0
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models—0
Criticality Leveraged Adversarial Training (CLAT) for Boosted Performance via Parameter Efficiency—0
Corruption-Robust Offline Reinforcement Learning—0
ALMA: Aggregated Lipschitz Maximization Attack on Auto-encoders—0
Contextual Fusion For Adversarial Robustness—0
Constraining Logits by Bounded Function for Adversarial Robustness—0
Constrained Learning with Non-Convex Losses—0
Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Data—0
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation—0
aiXamine: Simplified LLM Safety and Security—0
AI-Compass: A Comprehensive and Effective Multi-module Testing Tool for AI Systems—0
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks—0
Adversarial Attacks and Defenses for Speech Recognition Systems—0
CARES: Comprehensive Evaluation of Safety and Adversarial Robustness in Medical LLMs—0
Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI—0
Conflict-Aware Adversarial Training—0
A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models—0
A Holistic Assessment of the Reliability of Machine Learning Systems—0
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs—0
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness—0
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks—0
Collective Robustness Certificates—0
A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks—0
Clustering Effect of Adversarial Robust Models—0
A Fundamental Accuracy--Robustness Trade-off in Regression and Classification—0
A Frequency Perspective of Adversarial Robustness—0
Adversarial Prompt Distillation for Vision-Language Models—0
A Framework for Verification of Wasserstein Adversarial Robustness—0
Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information—0
Class-Aware Robust Adversarial Training for Object Detection—0
A Flat Minima Perspective on Understanding Augmentations and Model Robustness—0
Class-Aware Domain Adaptation for Improving Adversarial Robustness—0
A Finer Calibration Analysis for Adversarial Robustness—0
Characterizing the adversarial vulnerability of speech self-supervised learning—0
Affine-Invariant Robust Training—0
Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend—0
Chaos Theory and Adversarial Robustness—0
CGDTest: A Constrained Gradient Descent Algorithm for Testing Neural Networks—0
Certifying Robustness of Graph Laplacian Based Semi-Supervised Learning—0
Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness—0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning—0
Adversarial Masked Autoencoder Purifier with Defense Transferability—0
Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives—0
A Closer Look at the Adversarial Robustness of Information Bottleneck Models—0
Show:102550
← PrevPage 17 of 35Next →
All datasetsAdvGLUECIFAR-10ImageNetImageNet-AImageNet-CStylized ImageNetCIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeBERTa (single model)Accuracy0.61—Unverified
2ALBERT (single model)Accuracy0.59—Unverified
3T5 (single model)Accuracy0.57—Unverified
4SMART_RoBERTa (single model)Accuracy0.54—Unverified
5FreeLB (single model)Accuracy0.5—Unverified
6RoBERTa (single model)Accuracy0.5—Unverified
7InfoBERT (single model)Accuracy0.46—Unverified
8ELECTRA (single model)Accuracy0.42—Unverified
9BERT (single model)Accuracy0.34—Unverified
10SMART_BERT (single model)Accuracy0.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed classifierAccuracy95.23—Unverified
2Stochastic-LWTA/PGD/WideResNet-34-10Accuracy92.26—Unverified
3Stochastic-LWTA/PGD/WideResNet-34-5Accuracy91.88—Unverified
4GLOT-DRAccuracy84.13—Unverified
5TRADES-ANCRA/ResNet18Accuracy81.7—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (SGD, Cosine)Accuracy77.4—Unverified
2ResNet-50 (SGD, Step)Accuracy76.9—Unverified
3DeiT-S (AdamW, Cosine)Accuracy76.8—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy76.4—Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy12.2—Unverified
2ResNet-50 (SGD, Cosine)Accuracy3.3—Unverified
3ResNet-50 (SGD, Step)Accuracy3.2—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy3.1—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (AdamW, Cosine)mean Corruption Error (mCE)59.3—Unverified
2ResNet-50 (SGD, Step)mean Corruption Error (mCE)57.9—Unverified
3ResNet-50 (SGD, Cosine)mean Corruption Error (mCE)56.9—Unverified
4DeiT-S (AdamW, Cosine)mean Corruption Error (mCE)48—Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy13—Unverified
2ResNet-50 (SGD, Cosine)Accuracy8.4—Unverified
3ResNet-50 (SGD, Step)Accuracy8.3—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy8.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed ClassifierClean Accuracy85.21—Unverified
2ResNet18/MART-ANCRAClean Accuracy60.1—Unverified