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 1401–1425 of 1746 papers

Title	Date	Tasks	Status
Buffer Zone based Defense against Adversarial Examples in Image Classification	Jan 1, 2021	Adversarial RobustnessClassification	—Unverified
What are effective labels for augmented data? Improving robustness with AutoLabel	Jan 1, 2021	Adversarial RobustnessData Augmentation	—Unverified
Perturbation Type Categorization for Multiple _p Bounded Adversarial Robustness	Jan 1, 2021	Adversarial RobustnessVocal Bursts Type Prediction	—Unverified
Disentangling Adversarial Robustness in Directions of the Data Manifold	Jan 1, 2021	Adversarial Robustness	CodeCode Available
Intriguing class-wise properties of adversarial training	Jan 1, 2021	Adversarial Robustness	—Unverified
No Feature Is An Island: Adaptive Collaborations Between Features Improve Adversarial Robustness	Jan 1, 2021	Adversarial Robustnesscounterfactual	—Unverified
Test-Time Adaptation and Adversarial Robustness	Jan 1, 2021	Adversarial RobustnessDomain Adaptation	—Unverified
Collective Robustness Certificates	Jan 1, 2021	Adversarial RobustnessImage Segmentation	—Unverified
Improving Adversarial Robustness in Weight-quantized Neural Networks	Dec 29, 2020	Adversarial RobustnessQuantization	—Unverified
A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning	Dec 25, 2020	Adversarial RobustnessAll	—Unverified
Gradient-Free Adversarial Attacks for Bayesian Neural Networks	Dec 23, 2020	Adversarial RobustnessBayesian Inference	CodeCode Available
Self-Progressing Robust Training	Dec 22, 2020	Adversarial Robustness	CodeCode Available
Sample Complexity of Adversarially Robust Linear Classification on Separated Data	Dec 19, 2020	Adversarial RobustnessClassification	—Unverified
Adversarially Robust Estimate and Risk Analysis in Linear Regression	Dec 18, 2020	Adversarial Robustnessregression	—Unverified
On the human-recognizability phenomenon of adversarially trained deep image classifiers	Dec 18, 2020	Adversarial Robustness	CodeCode Available
Generate and Verify: Semantically Meaningful Formal Analysis of Neural Network Perception Systems	Dec 16, 2020	Adversarial Robustness	—Unverified
A case for new neural network smoothness constraints	Dec 14, 2020	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
Achieving Adversarial Robustness Requires An Active Teacher	Dec 14, 2020	Adversarial Robustness	—Unverified
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints	Dec 14, 2020	Adversarial Robustness	CodeCode Available
Learning Energy-Based Models With Adversarial Training	Dec 11, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available
On 1/n neural representation and robustness	Dec 8, 2020	Adversarial Robustness	CodeCode Available
Overcomplete Representations Against Adversarial Videos	Dec 8, 2020	Adversarial RobustnessDecoder	CodeCode Available
Evaluating adversarial robustness in simulated cerebellum	Dec 5, 2020	Adversarial Robustness	—Unverified
Unsupervised Adversarially-Robust Representation Learning on Graphs	Dec 4, 2020	Adversarial RobustnessCommunity Detection	—Unverified
Stochastic Gradient Descent with Nonlinear Conjugate Gradient-Style Adaptive Momentum	Dec 3, 2020	Adversarial Robustness	—Unverified

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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