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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 601–650 of 1746 papers

TitleStatusHype
Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships—0
Robust Entropy Search for Safe Efficient Bayesian OptimizationCode0
Towards Unified Robustness Against Both Backdoor and Adversarial AttacksCode0
TIMA: Text-Image Mutual Awareness for Balancing Zero-Shot Adversarial Robustness and Generalization Ability—0
The Uncanny Valley: Exploring Adversarial Robustness from a Flatness PerspectiveCode0
Spectral regularization for adversarially-robust representation learningCode0
Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness—0
Can Implicit Bias Imply Adversarial Robustness?—0
Large Language Model Sentinel: LLM Agent for Adversarial Purification—0
Evaluating and Safeguarding the Adversarial Robustness of Retrieval-Based In-Context LearningCode0
Certified Robustness against Sparse Adversarial Perturbations via Data Localization—0
Adversarial Training via Adaptive Knowledge Amalgamation of an Ensemble of Teachers—0
Adaptive Batch Normalization Networks for Adversarial Robustness—0
Revisiting the Robust Generalization of Adversarial Prompt Tuning—0
Adversarial Robustness Guarantees for Quantum Classifiers—0
Adversarial Robustness for Visual Grounding of Multimodal Large Language ModelsCode0
SpeechGuard: Exploring the Adversarial Robustness of Multimodal Large Language Models—0
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation—0
The Pitfalls and Promise of Conformal Inference Under Adversarial AttacksCode0
On the Adversarial Robustness of Learning-based Image Compression Against Rate-Distortion Attacks—0
Evaluating Adversarial Robustness in the Spatial Frequency Domain—0
Assessing Adversarial Robustness of Large Language Models: An Empirical Study—0
Impact of Architectural Modifications on Deep Learning Adversarial RobustnessCode0
Robust Decentralized Learning with Local Updates and Gradient Tracking—0
Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing—0
Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal PerspectiveCode0
PAODING: A High-fidelity Data-free Pruning Toolkit for Debloating Pre-trained Neural Networks—0
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature AttacksCode0
Towards Robust Recommendation: A Review and an Adversarial Robustness Evaluation LibraryCode0
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks—0
A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models—0
Adversarial Robustness of Deep Learning-Based Malware Detectors via (De)Randomized SmoothingCode0
Fermi-Bose Machine achieves both generalization and adversarial robustness—0
GenFighter: A Generative and Evolutive Textual Attack Removal—0
SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam DetectionCode0
A Survey of Neural Network Robustness Assessment in Image Recognition—0
Struggle with Adversarial Defense? Try Diffusion—0
Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers—0
Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data—0
On adversarial training and the 1 Nearest Neighbor classifierCode0
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey—0
Investigating the Impact of Quantization on Adversarial Robustness—0
Evaluating Adversarial Robustness: A Comparison Of FGSM, Carlini-Wagner Attacks, And The Role of Distillation as Defense Mechanism—0
DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models—0
On Extending the Automatic Test Markup Language (ATML) for Machine Learning—0
Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks—0
On Inherent Adversarial Robustness of Active Vision Systems—0
Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning—0
Scalable Lipschitz Estimation for CNNs—0
Boosting Adversarial Training via Fisher-Rao Norm-based RegularizationCode0
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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