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 126–150 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial Robustness Against the Union of Multiple Perturbation Models	Sep 9, 2019	Adversarial Robustness	CodeCode Available	1	5
Bag of Tricks for Adversarial Training	Oct 1, 2020	Adversarial RobustnessBenchmarking	CodeCode Available	1	5
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness	Mar 20, 2023	Adversarial RobustnessMalware Detection	CodeCode Available	1	5
Benchmarking Adversarial Robustness on Image Classification	Jun 1, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
CARBEN: Composite Adversarial Robustness Benchmark	Jul 16, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Attacks on ML Defense Models Competition	Oct 15, 2021	Adversarial AttackAdversarial 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
Adversarial Robustness of Deep Convolutional Candlestick Learner	May 29, 2020	Adversarial Robustness	CodeCode Available	1	5
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective	Aug 21, 2022	Adversarial RobustnessData Augmentation	CodeCode Available	1	5
A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space	Dec 2, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Robustness for Code	Feb 11, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available	1	5
Adversarial Robustness on In- and Out-Distribution Improves Explainability	Mar 20, 2020	Adversarial Robustnessimage-classification	CodeCode Available	1	5
BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks	Apr 1, 2024	Adversarial RobustnessAutonomous Driving	CodeCode Available	1	5
Adversarial Robustness of Representation Learning for Knowledge Graphs	Sep 30, 2022	Adversarial RobustnessData Poisoning	CodeCode Available	1	5
Benchmarking and Analyzing Robust Point Cloud Recognition: Bag of Tricks for Defending Adversarial Examples	Jul 31, 2023	Adversarial RobustnessBenchmarking	CodeCode Available	1	5
Bispectral Neural Networks	Sep 7, 2022	Adversarial RobustnessRepresentation Learning	CodeCode Available	1	5
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness	Apr 30, 2020	Adversarial Robustness	CodeCode Available	1	5
A Unified Game-Theoretic Interpretation of Adversarial Robustness	Nov 5, 2021	Adversarial Robustness	CodeCode Available	1	5
Adversarial Contrastive Learning via Asymmetric InfoNCE	Jul 18, 2022	Adversarial RobustnessContrastive Learning	CodeCode Available	1	5
CausalAdv: Adversarial Robustness through the Lens of Causality	Jun 11, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Cauchy-Schwarz Divergence Information Bottleneck for Regression	Apr 27, 2024	Adversarial RobustnessInformation Plane	CodeCode Available	1	5
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies	Apr 14, 2024	Adversarial Robustness	CodeCode Available	1	5
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach	Oct 10, 2023	Adversarial Robustness	CodeCode Available	1	5
Certified Training: Small Boxes are All You Need	Oct 10, 2022	Adversarial RobustnessAll	CodeCode Available	1	5
Adversarial Robustness as a Prior for Learned Representations	Jun 3, 2019	Adversarial Robustness	CodeCode Available	1	5

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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