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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
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
Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion ModelsCode1
SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam DetectionCode0
Adversarial Robustness Limits via Scaling-Law and Human-Alignment StudiesCode1
Struggle with Adversarial Defense? Try Diffusion—0
A Survey of Neural Network Robustness Assessment in Image Recognition—0
Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers—0
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples RegularizationCode1
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
ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red TeamingCode2
DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models—0
Evaluating Adversarial Robustness: A Comparison Of FGSM, Carlini-Wagner Attacks, And The Role of Distillation as Defense Mechanism—0
On Extending the Automatic Test Markup Language (ATML) for Machine Learning—0
Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks—0
BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression TasksCode1
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
Ensemble Adversarial Defense via Integration of Multiple Dispersed Low Curvature Models—0
Towards Adversarial Robustness And Backdoor Mitigation in SSLCode0
Few-Shot Adversarial Prompt Learning on Vision-Language ModelsCode1
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset DistillationCode0
Certified Robustness to Clean-Label Poisoning Using Diffusion Denoising—0
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm RegularizationCode1
Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSMCode0
Understanding Robustness of Visual State Space Models for Image ClassificationCode0
Improving Adversarial Transferability of Vision-Language Pre-training Models through Collaborative Multimodal Interaction—0
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks—0
Towards Adversarially Robust Dataset Distillation by Curvature RegularizationCode0
Robust Subgraph Learning by Monitoring Early Training Representations—0
Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement—0
Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and EfficiencyCode0
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning—0
PeerAiD: Improving Adversarial Distillation from a Specialized Peer TutorCode1
Speech Robust Bench: A Robustness Benchmark For Speech RecognitionCode1
Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume—0
DF-RAP: A Robust Adversarial Perturbation for Defending against Deepfakes in Real-world Social Network ScenariosCode1
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language ModelsCode2
Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense—0
Catastrophic Overfitting: A Potential Blessing in Disguise—0
Extreme Miscalibration and the Illusion of Adversarial Robustness—0
Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates—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