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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 901–950 of 1746 papers

TitleStatusHype
Provable Defense Against Clustering Attacks on 3D Point Clouds—0
Provable Unrestricted Adversarial Training without Compromise with Generalizability—0
Provably Robust Transfer—0
Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection—0
Q-TART: Quickly Training for Adversarial Robustness and in-Transferability—0
QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks—0
Quantifying Adversarial Sensitivity of a Model as a Function of the Image Distribution—0
Quantitative Analysis of Deeply Quantized Tiny Neural Networks Robust to Adversarial Attacks—0
Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses—0
Quantum Support Vector Regression for Robust Anomaly Detection—0
Query complexity of adversarial attacks—0
Raising the Bar for Certified Adversarial Robustness with Diffusion Models—0
Random Entangled Tokens for Adversarially Robust Vision Transformer—0
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators—0
Random Projections for Improved Adversarial Robustness—0
Rapid Response: Mitigating LLM Jailbreaks with a Few Examples—0
RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias—0
Achieving More Human Brain-Like Vision via Human EEG Representational Alignment—0
Reassessing Noise Augmentation Methods in the Context of Adversarial Speech—0
RECAST: Interactive Auditing of Automatic Toxicity Detection Models—0
Recent Advances in Adversarial Training for Adversarial Robustness—0
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks—0
Recommender Systems for Democracy: Toward Adversarial Robustness in Voting Advice Applications—0
Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense—0
Reframing Neural Networks: Deep Structure in Overcomplete Representations—0
Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness—0
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training—0
Relating Adversarially Robust Generalization to Flat Minima—0
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence—0
Releasing Inequality Phenomena in L_-Adversarial Training via Input Gradient Distillation—0
Reliable and Efficient Evaluation of Adversarial Robustness for Deep Hashing-Based Retrieval—0
Removing Adversarial Noise in Class Activation Feature Space—0
Removing Out-of-Distribution Data Improves Adversarial Robustness—0
Rerouting LLM Routers—0
Residual Error: a New Performance Measure for Adversarial Robustness—0
Resilience to Multiple Attacks via Adversarially Trained MIMO Ensembles—0
Revisiting and Advancing Adversarial Training Through A Simple Baseline—0
Rethinking Audio-Visual Adversarial Vulnerability from Temporal and Modality Perspectives—0
Rethinking Classifier and Adversarial Attack—0
Rethinking Feature Uncertainty in Stochastic Neural Networks for Adversarial Robustness—0
Rethinking Non-idealities in Memristive Crossbars for Adversarial Robustness in Neural Networks—0
Rethinking the Adversarial Robustness of Multi-Exit Neural Networks in an Attack-Defense Game—0
Revisiting Adversarial Robustness of Classifiers With a Reject Option—0
Revisiting Robustness in Graph Machine Learning—0
Revisiting Role of Autoencoders in Adversarial Settings—0
Revisiting Semi-supervised Adversarial Robustness via Noise-aware Online Robust Distillation—0
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning—0
Revisiting the Robust Generalization of Adversarial Prompt Tuning—0
Robust and differentially private stochastic linear bandits—0
Robust and Private Learning of Halfspaces—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