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 1126–1150 of 1746 papers

Title	Date	Tasks	Status	Hype
Adversarial Robustness via Adaptive Label Smoothing	Sep 29, 2021	Adversarial Robustness	—Unverified	0
Efficient Certification for Probabilistic Robustness	Sep 29, 2021	Adversarial Robustness	—Unverified	0
Learning Sample Reweighting for Adversarial Robustness	Sep 29, 2021	Adversarial RobustnessBilevel Optimization	—Unverified	0
Towards Achieving Adversarial Robustness Beyond Perceptual Limits	Sep 29, 2021	Adversarial DefenseAdversarial Robustness	—Unverified	0
Resilience to Multiple Attacks via Adversarially Trained MIMO Ensembles	Sep 29, 2021	Adversarial Robustness	—Unverified	0
An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness	Sep 29, 2021	Adversarial RobustnessFairness	—Unverified	0
Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view Inconsistency	Sep 25, 2021	Adversarial Attack DetectionAdversarial Defense	CodeCode Available	0
CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks	Sep 22, 2021	Adversarial Robustness	CodeCode Available	0
Robust Physical-World Attacks on Face Recognition	Sep 20, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Simple Post-Training Robustness Using Test Time Augmentations and Random Forest	Sep 16, 2021	Adversarial RobustnessDiversity	CodeCode Available	0
Adversarial Examples for Evaluating Math Word Problem Solvers	Sep 13, 2021	Adversarial RobustnessMath	CodeCode Available	0
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
Adversarial Bone Length Attack on Action Recognition	Sep 13, 2021	Action RecognitionAdversarial Robustness	—Unverified	0
RobustART: Benchmarking Robustness on Architecture Design and Training Techniques	Sep 11, 2021	Adversarial RobustnessBenchmarking	CodeCode Available	1
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency	Sep 11, 2021	Adversarial RobustnessQuantization	—Unverified	0
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness	Sep 4, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Impact of Attention on Adversarial Robustness of Image Classification Models	Sep 2, 2021	Adversarial RobustnessClassification	—Unverified	0
Adversarial Robustness for Unsupervised Domain Adaptation	Sep 2, 2021	Adversarial RobustnessDomain Adaptation	—Unverified	0
Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings	Aug 30, 2021	Adversarial Robustness	—Unverified	0
Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks	Aug 26, 2021	Adversarial Robustness	—Unverified	0
A Hierarchical Assessment of Adversarial Severity	Aug 26, 2021	Adversarial Robustness	CodeCode Available	0
Generalized Real-World Super-Resolution through Adversarial Robustness	Aug 25, 2021	Adversarial RobustnessSuper-Resolution	CodeCode Available	1
Adversarially Robust One-class Novelty Detection	Aug 25, 2021	Adversarial RobustnessNovelty Detection	CodeCode Available	0
Bridged Adversarial Training	Aug 25, 2021	Adversarial Robustness	—Unverified	0
Are socially-aware trajectory prediction models really socially-aware?	Aug 24, 2021	Adversarial RobustnessAutonomous Navigation	CodeCode Available	1

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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