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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 1701–1746 of 1746 papers

TitleStatusHype
Flooding-X: Improving BERT's Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning—0
CE-based white-box adversarial attacks will not work using super-fitting—0
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning—0
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification—0
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations—0
Framework for Progressive Knowledge Fusion in Large Language Models Through Structured Conceptual Redundancy Analysis—0
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study—0
Frequency Regularization for Improving Adversarial Robustness—0
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks—0
From Sound Representation to Model Robustness—0
FullLoRA-AT: Efficiently Boosting the Robustness of Pretrained Vision Transformers—0
Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time—0
FuncFooler: A Practical Black-box Attack Against Learning-based Binary Code Similarity Detection Methods—0
Functional Network: A Novel Framework for Interpretability of Deep Neural Networks—0
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions—0
Function-Space Regularization for Deep Bayesian Classification—0
Function-Space Variational Inference for Deep Bayesian Classification—0
Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging—0
aw_nas: A Modularized and Extensible NAS framework—0
GARNET: A Spectral Approach to Robust and Scalable Graph Neural Networks—0
Average Margin Regularization for Classifiers—0
Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness—0
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification—0
Stochastic Gradient Descent with Nonlinear Conjugate Gradient-Style Adaptive Momentum—0
General Coded Computing: Adversarial Settings—0
Generalizability of Adversarial Robustness Under Distribution Shifts—0
Generalizable Deepfake Detection with Phase-Based Motion Analysis—0
Generalization Certificates for Adversarially Robust Bayesian Linear Regression—0
Generalization Error Analysis of Neural networks with Gradient Based Regularization—0
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness—0
Incorporating Hidden Layer representation into Adversarial Attacks and Defences—0
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness—0
AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework—0
Generalizing and Improving Jacobian and Hessian Regularization—0
Generate and Verify: Semantically Meaningful Formal Analysis of Neural Network Perception Systems—0
A Useful Taxonomy for Adversarial Robustness of Neural Networks—0
Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers—0
Adversarial Fine-tune with Dynamically Regulated Adversary—0
Generating Structured Adversarial Attacks Using Frank-Wolfe Method—0
Adversarial Examples on Segmentation Models Can be Easy to Transfer—0
GenFighter: A Generative and Evolutive Textual Attack Removal—0
GenLabel: Mixup Relabeling using Generative Models—0
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing—0
Stop Walking in Circles! Bailing Out Early in Projected Gradient Descent—0
GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks—0
StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection—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