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Adversarial Robustness

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

Papers

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Showing 51–100 of 1746 papers

TitleStatusHype
Fast and Low-Cost Genomic Foundation Models via Outlier RemovalCode1
OET: Optimization-based prompt injection Evaluation ToolkitCode1
Towards Robust LLMs: an Adversarial Robustness Measurement FrameworkCode0
Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends—0
Fast Adversarial Training with Weak-to-Strong Spatial-Temporal Consistency in the Frequency Domain on Videos—0
aiXamine: Simplified LLM Safety and Security—0
Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models—0
RDI: An adversarial robustness evaluation metric for deep neural networks based on model statistical featuresCode0
The Sword of Damocles in ViTs: Computational Redundancy Amplifies Adversarial Transferability—0
R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt TuningCode1
How to Enhance Downstream Adversarial Robustness (almost) without Touching the Pre-Trained Foundation Model?—0
Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks—0
Toward Spiking Neural Network Local Learning Modules Resistant to Adversarial Attacks—0
Adversarial Examples in Environment Perception for Automated Driving (Review)—0
Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-JudgeCode0
Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability—0
A Domain-Based Taxonomy of Jailbreak Vulnerabilities in Large Language Models—0
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models—0
A Study on Adversarial Robustness of Discriminative Prototypical LearningCode0
Bridging the Theoretical Gap in Randomized SmoothingCode0
AdPO: Enhancing the Adversarial Robustness of Large Vision-Language Models with Preference Optimization—0
Robust Unsupervised Domain Adaptation for 3D Point Cloud Segmentation Under Source Adversarial Attacks—0
ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networks—0
Lipschitz Constant Meets Condition Number: Learning Robust and Compact Deep Neural Networks—0
Feature Statistics with Uncertainty Help Adversarial RobustnessCode0
Stop Walking in Circles! Bailing Out Early in Projected Gradient Descent—0
Masks and Mimicry: Strategic Obfuscation and Impersonation Attacks on Authorship Verification—0
When is dataset cartography ineffective? Using training dynamics does not improve robustness against Adversarial SQuAD—0
Principal Eigenvalue Regularization for Improved Worst-Class Certified Robustness of Smoothed Classifiers—0
Robustness of deep learning classification to adversarial input on GPUs: asynchronous parallel accumulation is a source of vulnerability—0
Narrowing Class-Wise Robustness Gaps in Adversarial Training—0
On the Robustness Tradeoff in Fine-Tuning—0
MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models—0
Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory—0
Survey of Adversarial Robustness in Multimodal Large Language Models—0
Evolution-based Region Adversarial Prompt Learning for Robustness Enhancement in Vision-Language ModelsCode0
Robust Dataset Distillation by Matching Adversarial Trajectories—0
Robustness Tokens: Towards Adversarial Robustness of TransformersCode0
Quantitative Analysis of Deeply Quantized Tiny Neural Networks Robust to Adversarial Attacks—0
FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation MethodsCode0
Life-Cycle Routing Vulnerabilities of LLM Router—0
MMARD: Improving the Min-Max Optimization Process in Adversarial Robustness Distillation—0
Long-tailed Adversarial Training with Self-Distillation—0
Exploring Adversarial Transferability between Kolmogorov-arnold Networks—0
Adversarial Robustness of Discriminative Self-Supervised Learning in Vision—0
CLIP is Strong Enough to Fight Back: Test-time Counterattacks towards Zero-shot Adversarial Robustness of CLIPCode1
TAET: Two-Stage Adversarial Equalization Training on Long-Tailed DistributionsCode1
Transformer Meets Twicing: Harnessing Unattended Residual InformationCode0
Adversarial Robustness in Parameter-Space Classifiers—0
Evaluation of Hate Speech Detection Using Large Language Models and Geographical ContextualizationCode0
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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