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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 1601–1650 of 1746 papers

TitleStatusHype
Evaluating the Adversarial Robustness for Fourier Neural Operators—0
Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge—0
Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric—0
Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples—0
Evaluating the Adversarial Robustness of Detection Transformers—0
Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN—0
Evaluating the Evaluators: Trust in Adversarial Robustness Tests—0
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification—0
Singular Regularization with Information Bottleneck Improves Model's Adversarial Robustness—0
Evaluation Methodology for Attacks Against Confidence Thresholding Models—0
Understanding Adversarially Robust Generalization via Weight-Curvature Index—0
Evolutionary Reinforcement Learning: A Systematic Review and Future Directions—0
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions—0
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases—0
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures—0
Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification—0
Adversarial Learning with Cost-Sensitive Classes—0
Experimental robustness benchmark of quantum neural network on a superconducting quantum processor—0
Biased Multi-Domain Adversarial Training—0
Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks—0
Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks—0
Smoothing Policy Iteration for Zero-sum Markov Games—0
Smooth Kernels Improve Adversarial Robustness and Perceptually-Aligned Gradients—0
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness—0
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness—0
Exploiting Explainability to Design Adversarial Attacks and Evaluate Attack Resilience in Hate-Speech Detection Models—0
Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection—0
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring—0
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Adversarial Robustness—0
Smoothness Analysis of Adversarial Training—0
SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization—0
Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement—0
Beyond Pruning Criteria: The Dominant Role of Fine-Tuning and Adaptive Ratios in Neural Network Robustness—0
Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods—0
Exploring Adversarial Robustness of LiDAR-Camera Fusion Model in Autonomous Driving—0
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving—0
Exploring Adversarial Transferability between Kolmogorov-arnold Networks—0
Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness—0
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking—0
Exploring Biologically Inspired Mechanisms of Adversarial Robustness—0
Exploring Layerwise Adversarial Robustness Through the Lens of t-SNE—0
Exploring Robust Features for Improving Adversarial Robustness—0
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks—0
Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume—0
Exploring the Adversarial Robustness of CLIP for AI-generated Image Detection—0
Exploring the Back Alleys: Analysing The Robustness of Alternative Neural Network Architectures against Adversarial Attacks—0
Exploring the Hyperparameter Landscape of Adversarial Robustness—0
Exploring the Physical World Adversarial Robustness of Vehicle Detection—0
Exploring the Sharpened Cosine Similarity—0
Exponential Separation between Two Learning Models and Adversarial Robustness—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