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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 1251–1300 of 1746 papers

TitleStatusHype
Adversarial Robustness of Similarity-Based Link Prediction—0
Toward Spiking Neural Network Local Learning Modules Resistant to Adversarial Attacks—0
Prototypical Examples in Deep Learning: Metrics, Characteristics, and Utility—0
Pro-tuning: Unified Prompt Tuning for Vision Tasks—0
What are effective labels for augmented data? Improving robustness with AutoLabel—0
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More—0
Provable Defense Against Clustering Attacks on 3D Point Clouds—0
Provable Unrestricted Adversarial Training without Compromise with Generalizability—0
Adversarial Robustness of Program Synthesis Models—0
Towards Proving the Adversarial Robustness of Deep Neural Networks—0
Towards quantum enhanced adversarial robustness in machine learning—0
Provably Robust Transfer—0
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction—0
Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness—0
Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection—0
Towards Robust and Accurate Stability Estimation of Local Surrogate Models in Text-based Explainable AI—0
Adversarial Robustness of Partitioned Quantum Classifiers—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
Adversarial Robustness of In-Context Learning in Transformers for Linear Regression—0
Quantitative Analysis of Deeply Quantized Tiny Neural Networks Robust to Adversarial Attacks—0
Towards Robust and Accurate Visual Prompting—0
Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses—0
Quantum Support Vector Regression for Robust Anomaly Detection—0
Towards Robust Deep Neural Networks—0
Query complexity of adversarial attacks—0
Towards Robust Graph Contrastive Learning—0
Adversarial Robustness of Flow-Based Generative Models—0
Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers—0
Raising the Bar for Certified Adversarial Robustness with Diffusion Models—0
Towards Robust Image Classification Using Sequential Attention Models—0
Random Entangled Tokens for Adversarially Robust Vision Transformer—0
Who's Afraid of Thomas Bayes?—0
Towards Robustness of Deep Neural Networks via Regularization—0
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators—0
Random Projections for Improved Adversarial Robustness—0
Adversarial Robustness of Discriminative Self-Supervised Learning in Vision—0
Rapid Response: Mitigating LLM Jailbreaks with a Few Examples—0
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems—0
RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias—0
With Great Backbones Comes Great Adversarial Transferability—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
Adversarial Robustness of Deep Sensor Fusion Models—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
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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