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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 651–700 of 1746 papers

TitleStatusHype
Ensemble Adversarial Defense via Integration of Multiple Dispersed Low Curvature Models—0
Towards Adversarial Robustness And Backdoor Mitigation in SSLCode0
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset DistillationCode0
Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSMCode0
Certified Robustness to Clean-Label Poisoning Using Diffusion Denoising—0
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
Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and EfficiencyCode0
Robust Subgraph Learning by Monitoring Early Training Representations—0
Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement—0
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning—0
Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume—0
Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense—0
Catastrophic Overfitting: A Potential Blessing in Disguise—0
Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates—0
Extreme Miscalibration and the Illusion of Adversarial Robustness—0
A Curious Case of Remarkable Resilience to Gradient Attacks via Fully Convolutional and Differentiable Front End with a Skip Connection—0
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection—0
Spectrum Extraction and Clipping for Implicitly Linear LayersCode0
A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing—0
Distilling Adversarial Robustness Using Heterogeneous Teachers—0
Evolutionary Reinforcement Learning: A Systematic Review and Future Directions—0
Evaluating Adversarial Robustness of Low dose CT RecoveryCode0
A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual ModelsCode0
Maintaining Adversarial Robustness in Continuous Learning—0
DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation—0
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models—0
Reducing Texture Bias of Deep Neural Networks via Edge Enhancing DiffusionCode0
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring—0
Two Tales of Single-Phase Contrastive Hebbian LearningCode0
Tighter Bounds on the Information Bottleneck with Application to Deep LearningCode0
A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense—0
TETRIS: Towards Exploring the Robustness of Interactive Segmentation—0
RAMP: Boosting Adversarial Robustness Against Multiple l_p Perturbations for Universal RobustnessCode0
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs—0
Is Adversarial Training with Compressed Datasets Effective?Code0
Adversarial Robustness Through Artifact Design—0
Partially Recentralization Softmax Loss for Vision-Language Models Robustness—0
Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons—0
Causal Feature Selection for Responsible Machine Learning—0
Exploring Biologically Inspired Mechanisms of Adversarial Robustness—0
MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed ClassifiersCode0
Delving into Decision-based Black-box Attacks on Semantic Segmentation—0
Achieving More Human Brain-Like Vision via Human EEG Representational Alignment—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective—0
AFD: Mitigating Feature Gap for Adversarial Robustness by Feature DisentanglementCode0
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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