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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 351–400 of 1746 papers

TitleStatusHype
Erasing Concepts, Steering Generations: A Comprehensive Survey of Concept Suppression—0
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains—0
Curvature Dynamic Black-box Attack: revisiting adversarial robustness via dynamic curvature estimation—0
Enhancing Adversarial Robustness of Vision Language Models via Adversarial Mixture Prompt Tuning—0
Experimental robustness benchmark of quantum neural network on a superconducting quantum processor—0
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off—0
Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models—0
Lessons from Defending Gemini Against Indirect Prompt Injections—0
Recommender Systems for Democracy: Toward Adversarial Robustness in Voting Advice Applications—0
Adversarial Robustness for Unified Multi-Modal Encoders via Efficient Calibration—0
CARES: Comprehensive Evaluation of Safety and Adversarial Robustness in Medical LLMs—0
Adversarially Robust Spiking Neural Networks with Sparse Connectivity—0
Evaluating the Robustness of Adversarial Defenses in Malware Detection SystemsCode0
Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks—0
Unpacking Robustness in Inflectional Languages: Adversarial Evaluation and Mechanistic Insights—0
ALMA: Aggregated Lipschitz Maximization Attack on Auto-encoders—0
Adversarial Robustness Analysis of Vision-Language Models in Medical Image SegmentationCode0
Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR ImagesCode0
Quantum Support Vector Regression for Robust Anomaly Detection—0
Towards Robust LLMs: an Adversarial Robustness Measurement FrameworkCode0
aiXamine: Simplified LLM Safety and Security—0
Fast Adversarial Training with Weak-to-Strong Spatial-Temporal Consistency in the Frequency Domain on Videos—0
Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends—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
How to Enhance Downstream Adversarial Robustness (almost) without Touching the Pre-Trained Foundation Model?—0
The Sword of Damocles in ViTs: Computational Redundancy Amplifies Adversarial Transferability—0
Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks—0
Adversarial Examples in Environment Perception for Automated Driving (Review)—0
Toward Spiking Neural Network Local Learning Modules Resistant to Adversarial Attacks—0
Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-JudgeCode0
A Domain-Based Taxonomy of Jailbreak Vulnerabilities in Large Language Models—0
Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability—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
Lipschitz Constant Meets Condition Number: Learning Robust and Compact Deep Neural Networks—0
Feature Statistics with Uncertainty Help Adversarial RobustnessCode0
ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networks—0
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
Robustness of deep learning classification to adversarial input on GPUs: asynchronous parallel accumulation is a source of vulnerability—0
Principal Eigenvalue Regularization for Improved Worst-Class Certified Robustness of Smoothed Classifiers—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
Survey of Adversarial Robustness in Multimodal Large Language Models—0
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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