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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 301–350 of 1746 papers

TitleStatusHype
Improving Adversarial Robustness via Decoupled Visual Representation MaskingCode0
Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step DefencesCode0
Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis—0
An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare RecordsCode2
Robust Information Retrieval—0
Improving Adversarial Robustness via Feature Pattern Consistency Constraint—0
Towards Evaluating the Robustness of Visual State Space ModelsCode1
On Evaluating Adversarial Robustness of Volumetric Medical Segmentation ModelsCode1
Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness—0
Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World AttacksCode1
Large Language Model Assisted Adversarial Robustness Neural Architecture SearchCode0
Exploring Adversarial Robustness of Deep State Space ModelsCode1
Improving Alignment and Robustness with Circuit BreakersCode3
Reproducibility Study on Adversarial Attacks Against Robust Transformer TrackersCode0
Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular DataCode1
Enhancing Adversarial Robustness in SNNs with Sparse Gradients—0
Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships—0
Robust Entropy Search for Safe Efficient Bayesian OptimizationCode0
Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI—0
White-box Multimodal Jailbreaks Against Large Vision-Language ModelsCode1
Towards Unified Robustness Against Both Backdoor and Adversarial AttacksCode0
TIMA: Text-Image Mutual Awareness for Balancing Zero-Shot Adversarial Robustness and Generalization Ability—0
Spectral regularization for adversarially-robust representation learningCode0
The Uncanny Valley: Exploring Adversarial Robustness from a Flatness PerspectiveCode0
Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness—0
Large Language Model Sentinel: LLM Agent for Adversarial Purification—0
Can Implicit Bias Imply Adversarial Robustness?—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
The Pitfalls and Promise of Conformal Inference Under Adversarial AttacksCode0
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation—0
SpeechGuard: Exploring the Adversarial Robustness of Multimodal Large Language Models—0
On the Adversarial Robustness of Learning-based Image Compression Against Rate-Distortion Attacks—0
RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text DetectorsCode2
Evaluating Adversarial Robustness in the Spatial Frequency Domain—0
Universal Adversarial Perturbations for Vision-Language Pre-trained ModelsCode1
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
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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