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 1001–1025 of 1746 papers

Title	Date	Tasks	Status
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification	Oct 25, 2022	Adversarial RobustnessData Poisoning	—Unverified
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games	Oct 23, 2022	Adversarial Robustness	—Unverified
Evolution of Neural Tangent Kernels under Benign and Adversarial Training	Oct 21, 2022	Adversarial Robustness	CodeCode Available
Learning Sample Reweighting for Accuracy and Adversarial Robustness	Oct 20, 2022	Adversarial RobustnessBilevel Optimization	—Unverified
Chaos Theory and Adversarial Robustness	Oct 20, 2022	Adversarial Robustness	—Unverified
LOT: Layer-wise Orthogonal Training on Improving _2 Certified Robustness	Oct 20, 2022	Adversarial Robustness	CodeCode Available
Learning Transferable Adversarial Robust Representations via Multi-view Consistency	Oct 19, 2022	Adversarial AttackAdversarial Robustness	—Unverified
On the Adversarial Robustness of Mixture of Experts	Oct 19, 2022	Adversarial RobustnessMixture-of-Experts	—Unverified
ROSE: Robust Selective Fine-tuning for Pre-trained Language Models	Oct 18, 2022	Adversarial Robustness	CodeCode Available
Improving Adversarial Robustness by Contrastive Guided Diffusion Process	Oct 18, 2022	Adversarial RobustnessSynthetic Data Generation	—Unverified
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations	Oct 17, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available
RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks	Oct 11, 2022	Adversarial RobustnessGPU	CodeCode Available
Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization	Oct 11, 2022	Adversarial DefenseAdversarial Robustness	—Unverified
What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?	Oct 11, 2022	Adversarial Robustness	CodeCode Available
Symmetry Defense Against CNN Adversarial Perturbation Attacks	Oct 8, 2022	Adversarial RobustnessAutonomous Vehicles	—Unverified
Towards Out-of-Distribution Adversarial Robustness	Oct 6, 2022	Adversarial Robustness	CodeCode Available
On Adversarial Robustness of Deep Image Deblurring	Oct 5, 2022	Adversarial RobustnessDeblurring	—Unverified
Strength-Adaptive Adversarial Training	Oct 4, 2022	Adversarial RobustnessScheduling	—Unverified
Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection	Oct 3, 2022	Active Speaker DetectionAdversarial Robustness	—Unverified
Adaptive Smoothness-weighted Adversarial Training for Multiple Perturbations with Its Stability Analysis	Oct 2, 2022	Adversarial Robustness	CodeCode Available
Understanding Adversarial Robustness Against On-manifold Adversarial Examples	Oct 2, 2022	Adversarial Robustness	CodeCode Available
On the tightness of linear relaxation based robustness certification methods	Oct 1, 2022	Adversarial Robustness	—Unverified
Improving Robustness with Adaptive Weight Decay	Sep 30, 2022	Adversarial Robustness	CodeCode Available
Generalizability of Adversarial Robustness Under Distribution Shifts	Sep 29, 2022	Adversarial RobustnessDomain Generalization	—Unverified
Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training	Sep 27, 2022	Adversarial 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