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 301–325 of 1746 papers

Title	Date	Tasks	Status	Hype
HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds	Aug 20, 2023	Adversarial Robustness	CodeCode Available	1
Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100	Aug 5, 2021	Adversarial Robustness	CodeCode Available	1
Benchmarking and Analyzing Robust Point Cloud Recognition: Bag of Tricks for Defending Adversarial Examples	Jul 31, 2023	Adversarial RobustnessBenchmarking	CodeCode Available	1
How to Select One Among All? An Extensive Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding	Sep 13, 2021	Adversarial RobustnessAll	CodeCode Available	1
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal	Dec 7, 2021	Adversarial RobustnessSensitivity	CodeCode Available	1
HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness	Jul 21, 2023	Adversarial RobustnessData Augmentation	CodeCode Available	1
A Self-supervised Approach for Adversarial Robustness	Jun 8, 2020	Adversarial RobustnessGeneral Classification	CodeCode Available	1
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness	Jun 24, 2020	Adversarial Robustness	CodeCode Available	1
Improving Adversarial Robustness by Enforcing Local and Global Compactness	Jul 10, 2020	Adversarial RobustnessClustering	CodeCode Available	1
Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting	Aug 20, 2023	Adversarial Robustness	CodeCode Available	1
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies	Apr 14, 2024	Adversarial Robustness	CodeCode Available	1
Improving Adversarial Robustness via Channel-wise Activation Suppressing	Mar 11, 2021	Adversarial Robustness	CodeCode Available	1
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration	Mar 23, 2021	Adversarial RobustnessDimensionality Reduction	CodeCode Available	1
Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization	Oct 12, 2023	Adversarial RobustnessHyperparameter Optimization	CodeCode Available	1
Feature Separation and Recalibration for Adversarial Robustness	Mar 24, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness	Feb 7, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations	Mar 23, 2020	Adversarial RobustnessAttribute	CodeCode Available	1
Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuning	Dec 31, 2020	Adversarial RobustnessData Augmentation	CodeCode Available	1
Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4	Mar 8, 2022	Adversarial Robustness	CodeCode Available	1
Large Language Models to Identify Social Determinants of Health in Electronic Health Records	Aug 11, 2023	Adversarial RobustnessEthics	CodeCode Available	1
On the Adversarial Robustness of Vision Transformers	Mar 29, 2021	Adversarial Robustness	CodeCode Available	1
Regularization with Latent Space Virtual Adversarial Training	Nov 26, 2020	Adversarial Robustnessimage-classification	CodeCode Available	1
LyaNet: A Lyapunov Framework for Training Neural ODEs	Feb 5, 2022	Adversarial Robustness	CodeCode Available	1
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network	Apr 6, 2022	Adversarial RobustnessModel Compression	CodeCode Available	1
Analyzing Adversarial Robustness of Deep Neural Networks in Pixel Space: a Semantic Perspective	Jun 18, 2021	Adversarial Robustness	—Unverified	0

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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