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 1101–1125 of 1746 papers

Title	Date	Tasks	Status
CalFAT: Calibrated Federated Adversarial Training with Label Skewness	May 30, 2022	Adversarial RobustnessFederated Learning	CodeCode Available
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection	May 30, 2022	Adversarial RobustnessAndroid Malware Detection	CodeCode Available
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models	May 30, 2022	Adversarial AttackAdversarial Robustness	—Unverified
Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction	May 27, 2022	Adversarial RobustnessDecision Making	—Unverified
Functional Network: A Novel Framework for Interpretability of Deep Neural Networks	May 24, 2022	Adversarial RobustnessTopological Data Analysis	—Unverified
Squeeze Training for Adversarial Robustness	May 23, 2022	Adversarial Robustness	CodeCode Available
Hierarchical Distribution-Aware Testing of Deep Learning	May 17, 2022	Adversarial RobustnessData Compression	CodeCode Available
Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection	May 15, 2022	Adversarial Robustness	—Unverified
Evaluating Membership Inference Through Adversarial Robustness	May 14, 2022	Adversarial RobustnessDeep Learning	CodeCode Available
Sibylvariant Transformations for Robust Text Classification	May 10, 2022	Adversarial RobustnessDefect Detection	CodeCode Available
Can collaborative learning be private, robust and scalable?	May 5, 2022	Adversarial RobustnessFederated Learning	—Unverified
Structural Extensions of Basis Pursuit: Guarantees on Adversarial Robustness	May 5, 2022	Adversarial Robustness	—Unverified
Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs	May 4, 2022	Adversarial RobustnessDisentanglement	CodeCode Available
CE-based white-box adversarial attacks will not work using super-fitting	May 4, 2022	Adversarial AttackAdversarial Robustness	—Unverified
Rethinking Classifier and Adversarial Attack	May 4, 2022	Adversarial AttackAdversarial Robustness	—Unverified
MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer	May 2, 2022	Adversarial Robustnessimage-classification	—Unverified
Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples	May 1, 2022	Adversarial Robustness	—Unverified
Adversarial Fine-tune with Dynamically Regulated Adversary	Apr 28, 2022	Adversarial AttackAdversarial Robustness	—Unverified
On Fragile Features and Batch Normalization in Adversarial Training	Apr 26, 2022	Adversarial Robustness	—Unverified
Testing robustness of predictions of trained classifiers against naturally occurring perturbations	Apr 21, 2022	Adversarial AttackAdversarial Robustness	—Unverified
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning	Apr 15, 2022	Adversarial RobustnessAutonomous Driving	—Unverified
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks	Apr 14, 2022	Adversarial AttackAdversarial Robustness	—Unverified
Q-TART: Quickly Training for Adversarial Robustness and in-Transferability	Apr 14, 2022	Adversarial Robustness	—Unverified
Planting Undetectable Backdoors in Machine Learning Models	Apr 14, 2022	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
A Simple Approach to Adversarial Robustness in Few-shot Image Classification	Apr 11, 2022	Adversarial RobustnessFew-Shot Image Classification	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