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 1051–1100 of 1746 papers

TitleStatusHype
MMARD: Improving the Min-Max Optimization Process in Adversarial Robustness Distillation—0
MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models—0
A More Biologically Plausible Local Learning Rule for ANNs—0
Towards Adversarially Robust Deep Image Denoising—0
Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness—0
A margin-based replacement for cross-entropy loss—0
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation—0
Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples—0
Model Unlearning via Sparse Autoencoder Subspace Guided Projections—0
MOREL: Enhancing Adversarial Robustness through Multi-Objective Representation Learning—0
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications—0
Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends—0
ALMA: Aggregated Lipschitz Maximization Attack on Auto-encoders—0
Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques—0
Multi-Scale Architectures Matter: On the Adversarial Robustness of Flow-based Lossless Compression—0
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation—0
Multi-stage Optimization based Adversarial Training—0
aiXamine: Simplified LLM Safety and Security—0
Multi-view Representation Learning from Malware to Defend Against Adversarial Variants—0
NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models—0
Narrowing Class-Wise Robustness Gaps in Adversarial Training—0
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games—0
Nearly Solved? Robust Deepfake Detection Requires More than Visual Forensics—0
AI-Compass: A Comprehensive and Effective Multi-module Testing Tool for AI Systems—0
Neural Architecture Dilation for Adversarial Robustness—0
Neural Belief Reasoner—0
Unlabeled Data Help: Minimax Analysis and Adversarial Robustness—0
A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models—0
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification—0
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception—0
A Holistic Assessment of the Reliability of Machine Learning Systems—0
Towards Adversarial Robustness of Deep Vision Algorithms—0
New CleverHans Feature: Better Adversarial Robustness Evaluations with Attack Bundling—0
Fundamental Limits in Formal Verification of Message-Passing Neural Networks—0
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs—0
No Feature Is An Island: Adaptive Collaborations Between Features Improve Adversarial Robustness—0
Unpacking Robustness in Inflectional Languages: Adversarial Evaluation and Mechanistic Insights—0
Non-Determinism in Neural Networks for Adversarial Robustness—0
Towards Adversarial Robustness via Transductive Learning—0
Non-Singular Adversarial Robustness of Neural Networks—0
Towards Adversarial Robustness via Debiased High-Confidence Logit Alignment—0
A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks—0
A Fundamental Accuracy--Robustness Trade-off in Regression and Classification—0
Unreasonable Effectiveness of Last Hidden Layer Activations for Adversarial Robustness—0
Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models—0
Towards Assessment of Randomized Smoothing Mechanisms for Certifying Adversarial Robustness—0
On adversarial robustness and the use of Wasserstein ascent-descent dynamics to enforce it—0
On Adversarial Robustness of Deep Image Deblurring—0
On Adversarial Robustness of Language Models in Transfer Learning—0
On Adversarial Robustness of Small vs Large Batch Training—0
Show:102550
← PrevPage 22 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