Adversarial Robustness

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

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1526–1550 of 1746 papers

Title	Date	Tasks	Status
Initialization Matters for Adversarial Transfer Learning	Dec 10, 2023	Adversarial Robustnessimage-classification	CodeCode Available
Automated Repair of Neural Networks	Jul 17, 2022	Adversarial Robustness	CodeCode Available
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy	Jun 16, 2019	Adversarial Robustness	CodeCode Available
Deterministic Gaussian Averaged Neural Networks	Jun 10, 2020	Adversarial Robustnessregression	CodeCode Available
Reducing Texture Bias of Deep Neural Networks via Edge Enhancing Diffusion	Feb 14, 2024	Adversarial RobustnessDomain Generalization	CodeCode Available
What Do Adversarially Robust Models Look At?	May 19, 2019	Adversarial RobustnessOpen-Ended Question Answering	CodeCode Available
Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing	Sep 8, 2023	Adversarial Robustnessregression	CodeCode Available
On Adversarial Robustness of Point Cloud Semantic Segmentation	Dec 11, 2021	Adversarial RobustnessAutonomous Driving	CodeCode Available
Adversarial Robustness of VAEs across Intersectional Subgroups	Jul 4, 2024	Adversarial RobustnessDimensionality Reduction	CodeCode Available
Adversarial Robustness of Supervised Sparse Coding	Oct 22, 2020	Adversarial Robustness	CodeCode Available
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients	Sep 28, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available
Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples	Jun 7, 2022	Adversarial Robustness	CodeCode Available
A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (TRASHFIRE)	Jan 24, 2024	Adversarial Robustness	CodeCode Available
Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks	Feb 26, 2020	Adversarial RobustnessAll	CodeCode Available
Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding	Jun 19, 2023	Adversarial RobustnessFew-Shot Learning	CodeCode Available
Investigating the Corruption Robustness of Image Classifiers with Random Lp-norm Corruptions	May 9, 2023	Adversarial RobustnessData Augmentation	CodeCode Available
A Study on Adversarial Robustness of Discriminative Prototypical Learning	Apr 3, 2025	Adversarial Robustness	CodeCode Available
Towards the first adversarially robust neural network model on MNIST	May 23, 2018	Adversarial RobustnessBinarization	CodeCode Available
Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow	Oct 26, 2023	Adversarial RobustnessMotion Detection	CodeCode Available
Dense Hopfield Networks in the Teacher-Student Setting	Jan 8, 2024	Adversarial Robustness	CodeCode Available
Is Adversarial Training with Compressed Datasets Effective?	Feb 8, 2024	Adversarial RobustnessDataset Condensation	CodeCode Available
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers	Jun 12, 2019	Adversarial Robustness	CodeCode Available
Reliable Robustness Evaluation via Automatically Constructed Attack Ensembles	Nov 23, 2022	Adversarial Robustness	CodeCode Available
Demystifying the Adversarial Robustness of Random Transformation Defenses	Jun 18, 2022	Adversarial RobustnessAutonomous Vehicles	CodeCode Available
Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences	Jun 14, 2024	Adversarial Robustnessimage-classification	CodeCode Available

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All datasets AdvGLUE CIFAR-10 ImageNet ImageNet-A ImageNet-C Stylized ImageNet CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DeBERTa (single model)	Accuracy	0.61	—	Unverified
2	ALBERT (single model)	Accuracy	0.59	—	Unverified
3	T5 (single model)	Accuracy	0.57	—	Unverified
4	SMART_RoBERTa (single model)	Accuracy	0.54	—	Unverified
5	FreeLB (single model)	Accuracy	0.5	—	Unverified
6	RoBERTa (single model)	Accuracy	0.5	—	Unverified
7	InfoBERT (single model)	Accuracy	0.46	—	Unverified
8	ELECTRA (single model)	Accuracy	0.42	—	Unverified
9	BERT (single model)	Accuracy	0.34	—	Unverified
10	SMART_BERT (single model)	Accuracy	0.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed classifier	Accuracy	95.23	—	Unverified
2	Stochastic-LWTA/PGD/WideResNet-34-10	Accuracy	92.26	—	Unverified
3	Stochastic-LWTA/PGD/WideResNet-34-5	Accuracy	91.88	—	Unverified
4	GLOT-DR	Accuracy	84.13	—	Unverified
5	TRADES-ANCRA/ResNet18	Accuracy	81.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (SGD, Cosine)	Accuracy	77.4	—	Unverified
2	ResNet-50 (SGD, Step)	Accuracy	76.9	—	Unverified
3	DeiT-S (AdamW, Cosine)	Accuracy	76.8	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	76.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	12.2	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	3.3	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	3.2	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	3.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (AdamW, Cosine)	mean Corruption Error (mCE)	59.3	—	Unverified
2	ResNet-50 (SGD, Step)	mean Corruption Error (mCE)	57.9	—	Unverified
3	ResNet-50 (SGD, Cosine)	mean Corruption Error (mCE)	56.9	—	Unverified
4	DeiT-S (AdamW, Cosine)	mean Corruption Error (mCE)	48	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	13	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	8.4	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	8.3	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	8.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed Classifier	Clean Accuracy	85.21	—	Unverified
2	ResNet18/MART-ANCRA	Clean Accuracy	60.1	—	Unverified