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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 751–800 of 1746 papers

TitleStatusHype
A Robust Adversarial Ensemble with Causal (Feature Interaction) Interpretations for Image Classification—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
Improved Adversarial Robustness by Reducing Open Space Risk via Tent Activations—0
GARNET: A Spectral Approach to Robust and Scalable Graph Neural Networks—0
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition—0
Empirical Study of the Decision Region and Robustness in Deep Neural Networks—0
Accelerating Adversarial Perturbation by 50% with Semi-backward Propagation—0
Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks—0
General Coded Computing: Adversarial Settings—0
Emoti-Attack: Zero-Perturbation Adversarial Attacks on NLP Systems via Emoji Sequences—0
Generalizable Deepfake Detection with Phase-Based Motion Analysis—0
_1 Adversarial Robustness Certificates: a Randomized Smoothing Approach—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
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains—0
Generalizing and Improving Jacobian and Hessian Regularization—0
Generate and Verify: Semantically Meaningful Formal Analysis of Neural Network Perception Systems—0
Improve Adversarial Robustness via Weight Penalization on Classification Layer—0
SOAR: Second-Order Adversarial Regularization—0
MadNet: Using a MAD Optimization for Defending Against Adversarial Attacks—0
Generating Structured Adversarial Attacks Using Frank-Wolfe Method—0
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness—0
GenFighter: A Generative and Evolutive Textual Attack Removal—0
Eight challenges in developing theory of intelligence—0
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing—0
Training Graph Neural Networks Using Non-Robust Samples—0
GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks—0
Are Perceptually-Aligned Gradients a General Property of Robust Classifiers?—0
Global Adversarial Robustness Guarantees for Neural Networks—0
Bridged Adversarial Training—0
GNN-Ensemble: Towards Random Decision Graph Neural Networks—0
Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Field—0
GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization—0
Are models trained on temporally-continuous data streams more adversarially robust?—0
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks—0
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning—0
GridMix: Strong regularization through local context mapping—0
Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending against Poisoning Attacks—0
Guess First to Enable Better Compression and Adversarial Robustness—0
Guidance Through Surrogate: Towards a Generic Diagnostic Attack—0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons—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