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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 1001–1050 of 1746 papers

TitleStatusHype
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification—0
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games—0
Evolution of Neural Tangent Kernels under Benign and Adversarial TrainingCode0
Learning Sample Reweighting for Accuracy and Adversarial Robustness—0
Chaos Theory and Adversarial Robustness—0
LOT: Layer-wise Orthogonal Training on Improving _2 Certified RobustnessCode0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
On the Adversarial Robustness of Mixture of Experts—0
ROSE: Robust Selective Fine-tuning for Pre-trained Language ModelsCode0
Improving Adversarial Robustness by Contrastive Guided Diffusion Process—0
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual ExplanationsCode0
RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule NetworksCode0
Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization—0
What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?Code0
Symmetry Defense Against CNN Adversarial Perturbation Attacks—0
Towards Out-of-Distribution Adversarial RobustnessCode0
On Adversarial Robustness of Deep Image Deblurring—0
Strength-Adaptive Adversarial Training—0
Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection—0
Adaptive Smoothness-weighted Adversarial Training for Multiple Perturbations with Its Stability AnalysisCode0
Understanding Adversarial Robustness Against On-manifold Adversarial ExamplesCode0
On the tightness of linear relaxation based robustness certification methods—0
Improving Robustness with Adaptive Weight DecayCode0
Generalizability of Adversarial Robustness Under Distribution Shifts—0
Inducing Data Amplification Using Auxiliary Datasets in Adversarial TrainingCode0
Fair Robust Active Learning by Joint Inconsistency—0
Audit and Improve Robustness of Private Neural Networks on Encrypted Data—0
AdvDO: Realistic Adversarial Attacks for Trajectory Prediction—0
Towards Bridging the Performance Gaps of Joint Energy-based ModelsCode0
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness—0
Robust Transferable Feature Extractors: Learning to Defend Pre-Trained Networks Against White Box Adversaries—0
Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering—0
On the interplay of adversarial robustness and architecture components: patches, convolution and attention—0
PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial AttackCode0
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices—0
Adversarial Robustness for Tabular Data through Cost and Utility Awareness—0
FuncFooler: A Practical Black-box Attack Against Learning-based Binary Code Similarity Detection Methods—0
Multi-Scale Architectures Matter: On the Adversarial Robustness of Flow-based Lossless Compression—0
Robust Prototypical Few-Shot Organ Segmentation with Regularized Neural-ODEsCode0
Lower Difficulty and Better Robustness: A Bregman Divergence Perspective for Adversarial Training—0
GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks—0
Shortcut Learning of Large Language Models in Natural Language Understanding—0
Different Spectral Representations in Optimized Artificial Neural Networks and BrainsCode0
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning—0
Exploring Adversarial Robustness of Vision Transformers in the Spectral PerspectiveCode0
Two Heads are Better than One: Robust Learning Meets Multi-branch ModelsCode0
On the Privacy Effect of Data Enhancement via the Lens of MemorizationCode0
Self-Knowledge Distillation via Dropout—0
Adversarial robustness of VAEs through the lens of local geometryCode0
On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks—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