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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 501–550 of 1746 papers

TitleStatusHype
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection—0
A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models—0
Adversarial Robustness through Local Linearization—0
Deadwooding: Robust Global Pruning for Deep Neural Networks—0
Adversarial Robustness through Dynamic Ensemble Learning—0
Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time—0
A Domain-Based Taxonomy of Jailbreak Vulnerabilities in Large Language Models—0
Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume—0
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models—0
Facial Attributes: Accuracy and Adversarial Robustness—0
Feature Losses for Adversarial Robustness—0
Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks—0
Adversarial Robustness Through Artifact Design—0
Accelerating Adversarial Perturbation by 50% with Semi-backward Propagation—0
Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge—0
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures—0
Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples—0
Exploiting Explainability to Design Adversarial Attacks and Evaluate Attack Resilience in Hate-Speech Detection Models—0
Biased Multi-Domain Adversarial Training—0
Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks—0
Adversarial Robustness: Softmax versus Openmax—0
Beyond Pruning Criteria: The Dominant Role of Fine-Tuning and Adaptive Ratios in Neural Network Robustness—0
Adversarial Robustness Overestimation and Instability in TRADES—0
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification—0
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness—0
Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection—0
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions—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
Adversarial Robustness on Image Classification with k-means—0
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification—0
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off—0
Adversarial Robustness of Visual Dialog—0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
Experimental robustness benchmark of quantum neural network on a superconducting quantum processor—0
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness—0
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring—0
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective—0
Better Generalization with Adaptive Adversarial Training—0
Adversarial Learning with Cost-Sensitive Classes—0
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection—0
Adversarial Robustness of Streaming Algorithms through Importance Sampling—0
Evaluation Methodology for Attacks Against Confidence Thresholding Models—0
Evolutionary Reinforcement Learning: A Systematic Review and Future Directions—0
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks—0
Adversarial Purification with the Manifold Hypothesis—0
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks—0
Adversarial robustness of sparse local Lipschitz predictors—0
Evaluating the Evaluators: Trust in Adversarial Robustness Tests—0
Benchmarking Adversarial Robustness of Compressed Deep Learning Models—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