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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 1151–1200 of 1746 papers

TitleStatusHype
Unsupervised Adversarially-Robust Representation Learning on Graphs—0
On the Generalization Properties of Adversarial Training—0
Advancing Adversarial Training by Injecting Booster Signal—0
Towards Certifiable Adversarial Sample Detection—0
On the interplay of adversarial robustness and architecture components: patches, convolution and attention—0
Unveiling Project-Specific Bias in Neural Code Models—0
Towards Compact and Robust Deep Neural Networks—0
On the Local Complexity of Linear Regions in Deep ReLU Networks—0
On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes—0
Understanding Robust Overfitting from the Feature Generalization Perspective—0
Advancing Adversarial Robustness Through Adversarial Logit Update—0
AdPO: Enhancing the Adversarial Robustness of Large Vision-Language Models with Preference Optimization—0
On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms—0
Adversarial Robustness without Adversarial Training: A Teacher-Guided Curriculum Learning Approach—0
Adversarial Robustness: What fools you makes you stronger—0
On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network—0
Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory—0
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective—0
On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective—0
On the Robustness Tradeoff in Fine-Tuning—0
Towards Defending against Adversarial Examples via Attack-Invariant Features—0
On the Scaling of Robustness and Effectiveness in Dense Retrieval—0
On the Sensitivity and Stability of Model Interpretations—0
On the Sensitivity of Adversarial Robustness to Input Data Distributions—0
On the tightness of linear relaxation based robustness certification methods—0
Adversarial robustness via stochastic regularization of neural activation sensitivity—0
On the Trade-offs between Adversarial Robustness and Actionable Explanations—0
On the Transferability of Minimal Prediction Preserving Inputs in Question Answering—0
On the (Un-)Avoidability of Adversarial Examples—0
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix—0
On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains—0
On the Zero-shot Adversarial Robustness of Vision-Language Models: A Truly Zero-shot and Training-free Approach—0
Adversarial Robustness via Runtime Masking and Cleansing—0
Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey—0
Optimal Statistical Guaratees for Adversarially Robust Gaussian Classification—0
Towards Disentangling Non-Robust and Robust Components in Performance Metric—0
Optimising Neural Network Architectures for Provable Adversarial Robustness—0
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness—0
Optimization and Optimizers for Adversarial Robustness—0
Optimized Potential Initialization for Low-latency Spiking Neural Networks—0
Optimizing Robustness and Accuracy in Mixture of Experts: A Dual-Model Approach—0
Adversarial Robustness via Label-Smoothing—0
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense—0
A Domain-Based Taxonomy of Jailbreak Vulnerabilities in Large Language Models—0
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey—0
Output Perturbation for Differentially Private Convex Optimization: Faster and More General—0
Towards Efficient Formal Verification of Spiking Neural Network—0
Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis—0
PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization—0
Adversarial Robustness via Adaptive Label Smoothing—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