SOTAVerified

Adversarial Robustness

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.

Papers

Showing 701750 of 1746 papers

TitleStatusHype
Improving Adversarial Robustness with Hypersphere Embedding and Angular-based Regularizations0
Model-tuning Via Prompts Makes NLP Models Adversarially RobustCode0
Robustness-preserving Lifelong Learning via Dataset Condensation0
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output CodesCode0
Adversarial Attacks on Machine Learning in Embedded and IoT Platforms0
Demystifying Causal Features on Adversarial Examples and Causal Inoculation for Robust Network by Adversarial Instrumental Variable RegressionCode1
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks0
Single Image Backdoor Inversion via Robust Smoothed ClassifiersCode1
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases0
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking0
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators0
CBA: Contextual Background Attack against Optical Aerial Detection in the Physical WorldCode1
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective0
Delving into the Adversarial Robustness of Federated Learning0
A Novel Noise Injection-based Training Scheme for Better Model Robustness0
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions0
Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition0
Adversarial Contrastive Distillation with Adaptive Denoising0
XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars0
IB-RAR: Information Bottleneck as Regularizer for Adversarial RobustnessCode0
Robustness Implies Fairness in Causal Algorithmic RecourseCode0
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks0
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary TasksCode0
Exploring and Exploiting Decision Boundary Dynamics for Adversarial RobustnessCode1
Rethinking Robust Contrastive Learning from the Adversarial PerspectiveCode0
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasksCode1
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial DefenseCode0
Provably Bounding Neural Network PreimagesCode0
Hyperbolic Contrastive Learning0
CertViT: Certified Robustness of Pre-Trained Vision TransformersCode0
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World AttacksCode1
On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex0
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification0
Language-Driven Anchors for Zero-Shot Adversarial RobustnessCode0
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive SmoothingCode1
Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation0
On the Adversarial Robustness of Camera-based 3D Object DetectionCode1
Provable Unrestricted Adversarial Training without Compromise with Generalizability0
Phase-shifted Adversarial Training0
On adversarial robustness and the use of Wasserstein ascent-descent dynamics to enforce it0
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
Explaining Adversarial Robustness of Neural Networks from Clustering Effect PerspectiveCode0
Adversarial Robustness via Random Projection FiltersCode1
A New Dataset Based on Images Taken by Blind People for Testing the Robustness of Image Classification Models Trained for ImageNet CategoriesCode0
Revisiting Residual Networks for Adversarial RobustnessCode1
Boosting Accuracy and Robustness of Student Models via Adaptive Adversarial Distillation0
Guidance Through Surrogate: Towards a Generic Diagnostic Attack0
Frequency Regularization for Improving Adversarial Robustness0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeBERTa (single model)Accuracy0.61Unverified
2ALBERT (single model)Accuracy0.59Unverified
3T5 (single model)Accuracy0.57Unverified
4SMART_RoBERTa (single model)Accuracy0.54Unverified
5FreeLB (single model)Accuracy0.5Unverified
6RoBERTa (single model)Accuracy0.5Unverified
7InfoBERT (single model)Accuracy0.46Unverified
8ELECTRA (single model)Accuracy0.42Unverified
9BERT (single model)Accuracy0.34Unverified
10SMART_BERT (single model)Accuracy0.3Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed classifierAccuracy95.23Unverified
2Stochastic-LWTA/PGD/WideResNet-34-10Accuracy92.26Unverified
3Stochastic-LWTA/PGD/WideResNet-34-5Accuracy91.88Unverified
4GLOT-DRAccuracy84.13Unverified
5TRADES-ANCRA/ResNet18Accuracy81.7Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (SGD, Cosine)Accuracy77.4Unverified
2ResNet-50 (SGD, Step)Accuracy76.9Unverified
3DeiT-S (AdamW, Cosine)Accuracy76.8Unverified
4ResNet-50 (AdamW, Cosine)Accuracy76.4Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy12.2Unverified
2ResNet-50 (SGD, Cosine)Accuracy3.3Unverified
3ResNet-50 (SGD, Step)Accuracy3.2Unverified
4ResNet-50 (AdamW, Cosine)Accuracy3.1Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (AdamW, Cosine)mean Corruption Error (mCE)59.3Unverified
2ResNet-50 (SGD, Step)mean Corruption Error (mCE)57.9Unverified
3ResNet-50 (SGD, Cosine)mean Corruption Error (mCE)56.9Unverified
4DeiT-S (AdamW, Cosine)mean Corruption Error (mCE)48Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy13Unverified
2ResNet-50 (SGD, Cosine)Accuracy8.4Unverified
3ResNet-50 (SGD, Step)Accuracy8.3Unverified
4ResNet-50 (AdamW, Cosine)Accuracy8.1Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed ClassifierClean Accuracy85.21Unverified
2ResNet18/MART-ANCRAClean Accuracy60.1Unverified