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 1426–1450 of 1746 papers

Title	Date	Tasks	Status
Sorting out Lipschitz function approximation	Nov 13, 2018	Adversarial RobustnessGeneralization Bounds	CodeCode Available
Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary	May 6, 2020	Adversarial Robustness	CodeCode Available
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation	Feb 7, 2021	Adversarial RobustnessGraph Embedding	CodeCode Available
Hyper-parameter Tuning for Adversarially Robust Models	Apr 5, 2023	Adversarial RobustnessGPU	CodeCode Available
End-to-end Kernel Learning via Generative Random Fourier Features	Sep 10, 2020	Adversarial Robustness	CodeCode Available
IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound	Jun 29, 2022	Adversarial Robustness	CodeCode Available
IB-RAR: Information Bottleneck as Regularizer for Adversarial Robustness	Feb 9, 2023	Adversarial Robustness	CodeCode Available
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers	Aug 1, 2023	Adversarial Robustnessimage-classification	CodeCode Available
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses	Aug 20, 2019	Adversarial Robustness	CodeCode Available
SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection	Apr 15, 2024	Adversarial RobustnessBackdoor Attack	CodeCode Available
Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos	Jan 3, 2023	Action RecognitionAdversarial Robustness	CodeCode Available
Characterizing Data Point Vulnerability via Average-Case Robustness	Jul 26, 2023	Adversarial RobustnessMulti-class Classification	CodeCode Available
Model Compression with Adversarial Robustness: A Unified Optimization Framework	Feb 10, 2019	Adversarial RobustnessModel Compression	CodeCode Available
Impact of Architectural Modifications on Deep Learning Adversarial Robustness	May 3, 2024	Adversarial RobustnessDeep Learning	CodeCode Available
Provable Adversarial Robustness for Fractional Lp Threat Models	Mar 16, 2022	Adversarial Robustness	CodeCode Available
Towards Out-of-Distribution Adversarial Robustness	Oct 6, 2022	Adversarial Robustness	CodeCode Available
Spectral regularization for adversarially-robust representation learning	May 27, 2024	Adversarial RobustnessRepresentation Learning	CodeCode Available
Spectrum Extraction and Clipping for Implicitly Linear Layers	Feb 25, 2024	Adversarial Robustness	CodeCode Available
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations	Oct 17, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available
Implicit Generative Modeling of Random Noise during Training for Adversarial Robustness	Jul 5, 2018	Adversarial Robustness	CodeCode Available
Provably Bounding Neural Network Preimages	Feb 2, 2023	Adversarial RobustnessGPU	CodeCode Available
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks	Jun 8, 2019	Adversarial Robustness	CodeCode Available
Adversarially Robust Spiking Neural Networks Through Conversion	Nov 15, 2023	Adversarial Robustness	CodeCode Available
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing	Mar 24, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available
Adversarial Attack Generation Empowered by Min-Max Optimization	Jun 9, 2019	Adversarial AttackAdversarial Robustness	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