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 251–275 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial Robustness against Multiple and Single l_p-Threat Models via Quick Fine-Tuning of Robust Classifiers	May 26, 2021	Adversarial Robustness	CodeCode Available	1	5
Adversarial Attacks on Graph Classification via Bayesian Optimisation	Nov 4, 2021	Adversarial RobustnessBayesian Optimisation	CodeCode Available	1	5
Adversarial Robustness Against the Union of Multiple Perturbation Models	Sep 9, 2019	Adversarial Robustness	CodeCode Available	1	5
Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck	Apr 6, 2022	Adversarial Robustness	CodeCode Available	1	5
Efficient Exact Verification of Binarized Neural Networks	May 7, 2020	Adversarial Robustness	CodeCode Available	1	5
Engineering flexible machine learning systems by traversing functionally-invariant paths	Apr 30, 2022	Adversarial RobustnessContinual Learning	CodeCode Available	1	5
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness	Feb 6, 2023	Adversarial Robustness	CodeCode Available	1	5
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks	Oct 26, 2021	Adversarial Robustness	CodeCode Available	1	5
Adversarial Robustness as a Prior for Learned Representations	Jun 3, 2019	Adversarial Robustness	CodeCode Available	1	5
A Unified Game-Theoretic Interpretation of Adversarial Robustness	Mar 12, 2021	Adversarial Robustness	CodeCode Available	1	5
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow	Mar 24, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Efficient Image-to-Image Diffusion Classifier for Adversarial Robustness	Aug 16, 2024	Adversarial Robustnessimage-classification	CodeCode Available	1	5
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection	Mar 12, 2020	Adversarial RobustnessNovelty Detection	CodeCode Available	1	5
Enhancing Adversarial Robustness for Deep Metric Learning	Mar 2, 2022	Adversarial RobustnessMetric Learning	CodeCode Available	1	5
Enhancing Adversarial Robustness via Score-Based Optimization	Jul 10, 2023	Adversarial DefenseAdversarial Robustness	CodeCode Available	1	5
Enhancing Adversarial Robustness via Test-time Transformation Ensembling	Jul 29, 2021	Adversarial Robustness	CodeCode Available	1	5
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs	Oct 6, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classification	Oct 19, 2021	Adversarial RobustnessClassification	CodeCode Available	1	5
Adversarial Attacks on ML Defense Models Competition	Oct 15, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification	Nov 28, 2021	Adversarial RobustnessClassification	CodeCode Available	1	5
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches	Mar 7, 2022	Adversarial RobustnessBenchmarking	CodeCode Available	1	5
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks	May 19, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
On Evaluating Adversarial Robustness of Volumetric Medical Segmentation Models	Jun 12, 2024	Adversarial RobustnessMamba	CodeCode Available	1	5
Are Transformers More Robust Than CNNs?	Nov 10, 2021	Adversarial Robustness	CodeCode Available	1	5
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness	Dec 5, 2021	Adversarial AttackAdversarial Defense	CodeCode Available	1	5

Show:10 25 50

← PrevPage 11 of 70Next →

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