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Adversarial Robustness

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 551–600 of 1746 papers

TitleStatusHype
Adversarial Robustness of Similarity-Based Link Prediction—0
Fault Tolerance of Neural Networks in Adversarial Settings—0
Feature Averaging: An Implicit Bias of Gradient Descent Leading to Non-Robustness in Neural Networks—0
Adversarial Robustness of Program Synthesis Models—0
CE-based white-box adversarial attacks will not work using super-fitting—0
Adversarial Information Bottleneck—0
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning—0
A case for new neural networks smoothness constraints—0
Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness—0
Finding Dynamics Preserving Adversarial Winning Tickets—0
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study—0
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction—0
aw_nas: A Modularized and Extensible NAS framework—0
Average Margin Regularization for Classifiers—0
Adversarial Robustness of Partitioned Quantum Classifiers—0
Faithful Knowledge Distillation—0
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training—0
Fast Adversarial Training against Textual Adversarial Attacks—0
AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework—0
Adversarial Fine-tune with Dynamically Regulated Adversary—0
Fast Adversarial Training with Weak-to-Strong Spatial-Temporal Consistency in the Frequency Domain on Videos—0
A Useful Taxonomy for Adversarial Robustness of Neural Networks—0
A unifying framework for differentially private quantum algorithms—0
Adversarial Robustness of In-Context Learning in Transformers for Linear Regression—0
A case for new neural network smoothness constraints—0
AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance—0
Adversarial Robustness of Flow-Based Generative Models—0
Audit and Improve Robustness of Private Neural Networks on Encrypted Data—0
Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers—0
Adversarial Examples on Segmentation Models Can be Easy to Transfer—0
Fair Robust Active Learning by Joint Inconsistency—0
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples—0
Adversarial Robustness of Discriminative Self-Supervised Learning in Vision—0
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions—0
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems—0
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness—0
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices—0
Attacking Graph Classification via Bayesian Optimisation—0
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks—0
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective—0
ATRAS: Adversarially Trained Robust Architecture Search—0
ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networks—0
Adversarial Examples in Environment Perception for Automated Driving (Review)—0
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks—0
FADER: Fast Adversarial Example Rejection—0
A Theoretical Perspective on Subnetwork Contributions to Adversarial Robustness—0
A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing—0
AI Safety in Practice: Enhancing Adversarial Robustness in Multimodal Image Captioning—0
A Survey on Out-of-Distribution Evaluation of Neural NLP Models—0
A Survey on Explainable Deep Reinforcement Learning—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