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Adversarial Robustness

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1401–1450 of 1746 papers

TitleStatusHype
A case for new neural network smoothness constraints—0
Towards Model-Agnostic Adversarial Defenses using Adversarially Trained Autoencoders—0
Transfer of Adversarial Robustness Between Perturbation Types—0
Robust Physical-World Attacks on Face Recognition—0
Adversarial Prompt Distillation for Vision-Language Models—0
Adversarial Masked Autoencoder Purifier with Defense Transferability—0
AdvFunMatch: When Consistent Teaching Meets Adversarial Robustness—0
Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning—0
Certifying Robustness of Graph Laplacian Based Semi-Supervised Learning—0
Robust Regularization with Adversarial Labelling of Perturbed Samples—0
Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness—0
CGDTest: A Constrained Gradient Descent Algorithm for Testing Neural Networks—0
Chaos Theory and Adversarial Robustness—0
Characterizing the adversarial vulnerability of speech self-supervised learning—0
Certified Robustness to Word Substitution Attack with Differential Privacy—0
Certified Robustness against Sparse Adversarial Perturbations via Data Localization—0
Class-Aware Domain Adaptation for Improving Adversarial Robustness—0
Class-Aware Robust Adversarial Training for Object Detection—0
Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information—0
VIC-KD: Variance-Invariance-Covariance Knowledge Distillation to Make Keyword Spotting More Robust Against Adversarial Attacks—0
Certified Federated Adversarial Training—0
Adversarially Robust Video Perception by Seeing Motion—0
Clustering Effect of Adversarial Robust Models—0
Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits—0
Robust Subgraph Learning by Monitoring Early Training Representations—0
Collective Robustness Certificates—0
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks—0
Certified Adversarial Robustness via Anisotropic Randomized Smoothing—0
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness—0
Certified Adversarial Robustness Under the Bounded Support Set—0
Robust Survival Analysis with Adversarial Regularization—0
Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning—0
Robust Transferable Feature Extractors: Learning to Defend Pre-Trained Networks Against White Box Adversaries—0
Robust Transfer Learning with Pretrained Language Models through Adapters—0
Robust Unsupervised Domain Adaptation for 3D Point Cloud Segmentation Under Source Adversarial Attacks—0
Conflict-Aware Adversarial Training—0
Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI—0
Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing—0
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning—0
Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness—0
Certified Adversarial Robustness for Deep Reinforcement Learning—0
Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Data—0
Constrained Learning with Non-Convex Losses—0
Constraining Logits by Bounded Function for Adversarial Robustness—0
Contextual Fusion For Adversarial Robustness—0
Corruption-Robust Offline Reinforcement Learning—0
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines—0
Criticality Leveraged Adversarial Training (CLAT) for Boosted Performance via Parameter Efficiency—0
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models—0
Cross-Entropy Loss Functions: Theoretical Analysis and Applications—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