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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 1351–1400 of 1746 papers

TitleStatusHype
Robust and differentially private stochastic linear bandits—0
Robust and Private Learning of Halfspaces—0
Adversarial Robustness for Unified Multi-Modal Encoders via Efficient Calibration—0
Adversarial Robustness for Tabular Data through Cost and Utility Awareness—0
RobustBlack: Challenging Black-Box Adversarial Attacks on State-of-the-Art Defenses—0
Robust Certification for Laplace Learning on Geometric Graphs—0
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data—0
Robust Collective Classification against Structural Attacks—0
Robust Dataset Distillation by Matching Adversarial Trajectories—0
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning—0
Robust Decentralized Learning with Local Updates and Gradient Tracking—0
Robust Deep Learning Ensemble against Deception—0
Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoff—0
Robust Distillation via Untargeted and Targeted Intermediate Adversarial Samples—0
RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems—0
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack—0
Trace-Norm Adversarial Examples—0
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks—0
Variational Autoencoders: A Harmonic Perspective—0
Trading Inference-Time Compute for Adversarial Robustness—0
Robustified Domain Adaptation—0
Robust Information Retrieval—0
Your Classifier Can Do More: Towards Bridging the Gaps in Classification, Robustness, and Generation—0
Robust Linear Regression: Phase-Transitions and Precise Tradeoffs for General Norms—0
Adversarial Robustness for Deep Learning-based Wildfire Prediction Models—0
Adversarial Robustness Curves—0
Robust low-rank training via approximate orthonormal constraints—0
Adversarial Robustness Assessment of NeuroEvolution Approaches—0
Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection—0
Adversarial Robustness Across Representation Spaces—0
RobustMQ: Benchmarking Robustness of Quantized Models—0
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium—0
Robustness Against Adversarial Attacks via Learning Confined Adversarial Polytopes—0
A Cost-Aware Approach to Adversarial Robustness in Neural Networks—0
Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach—0
Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates—0
Variational Randomized Smoothing for Sample-Wise Adversarial Robustness—0
Robustness Implies Privacy in Statistical Estimation—0
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?—0
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks—0
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy—0
Robustness of deep learning classification to adversarial input on GPUs: asynchronous parallel accumulation is a source of vulnerability—0
Robustness of Explanation Methods for NLP Models—0
Testing robustness of predictions of trained classifiers against naturally occurring perturbations—0
Robustness Of Saak Transform Against Adversarial Attacks—0
Robustness-preserving Lifelong Learning via Dataset Condensation—0
Non-adversarial Robustness of Deep Learning Methods for Computer Vision—0
Training Robust Deep Neural Networks via Adversarial Noise Propagation—0
Training Safe Neural Networks with Global SDP Bounds—0
A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking—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