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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 901–950 of 1746 papers

TitleStatusHype
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution DetectionCode0
CAT:Collaborative Adversarial TrainingCode0
Verifying Properties of Tsetlin MachinesCode0
Enhancing Multiple Reliability Measures via Nuisance-extended Information BottleneckCode0
Improved Adversarial Training Through Adaptive Instance-wise Loss SmoothingCode0
Optimization and Optimizers for Adversarial Robustness—0
Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection—0
Revisiting DeepFool: generalization and improvementCode0
Reliable and Efficient Evaluation of Adversarial Robustness for Deep Hashing-Based Retrieval—0
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation—0
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense—0
GNN-Ensemble: Towards Random Decision Graph Neural Networks—0
It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness—0
Improving Adversarial Robustness with Hypersphere Embedding and Angular-based Regularizations—0
Model-tuning Via Prompts Makes NLP Models Adversarially RobustCode0
Robustness-preserving Lifelong Learning via Dataset Condensation—0
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output CodesCode0
Adversarial Attacks on Machine Learning in Embedded and IoT Platforms—0
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks—0
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking—0
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases—0
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators—0
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective—0
Delving into the Adversarial Robustness of Federated Learning—0
Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition—0
A Novel Noise Injection-based Training Scheme for Better Model Robustness—0
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions—0
Adversarial Contrastive Distillation with Adaptive Denoising—0
XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars—0
IB-RAR: Information Bottleneck as Regularizer for Adversarial RobustnessCode0
Robustness Implies Fairness in Causal Algorithmic RecourseCode0
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary TasksCode0
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks—0
Rethinking Robust Contrastive Learning from the Adversarial PerspectiveCode0
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial DefenseCode0
Hyperbolic Contrastive Learning—0
Provably Bounding Neural Network PreimagesCode0
CertViT: Certified Robustness of Pre-Trained Vision TransformersCode0
On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex—0
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification—0
Language-Driven Anchors for Zero-Shot Adversarial RobustnessCode0
Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation—0
Provable Unrestricted Adversarial Training without Compromise with Generalizability—0
Phase-shifted Adversarial Training—0
On adversarial robustness and the use of Wasserstein ascent-descent dynamics to enforce it—0
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural NetworksCode0
Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on VideosCode0
Towards Better Robustness against Common Corruptions for Unsupervised Domain AdaptationCode0
A New Dataset Based on Images Taken by Blind People for Testing the Robustness of Image Classification Models Trained for ImageNet CategoriesCode0
Explaining Adversarial Robustness of Neural Networks from Clustering Effect PerspectiveCode0
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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