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 1651–1675 of 1746 papers

Title	Date	Tasks	Status
Multiplicative Reweighting for Robust Neural Network Optimization	Feb 24, 2021	Adversarial Robustness	CodeCode Available
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings	Jun 6, 2023	Adversarial RobustnessFraud Detection	CodeCode Available
Confidence-aware Training of Smoothed Classifiers for Certified Robustness	Dec 18, 2022	Adversarial Robustness	CodeCode Available
The Ultimate Combo: Boosting Adversarial Example Transferability by Composing Data Augmentations	Dec 18, 2023	Adversarial RobustnessData Augmentation	CodeCode Available
Confidence-aware Denoised Fine-tuning of Off-the-shelf Models for Certified Robustness	Nov 13, 2024	Adversarial RobustnessDenoising	CodeCode Available
Improving Neural Network Robustness via Persistency of Excitation	Jun 3, 2021	Adversarial Robustnessparameter estimation	CodeCode Available
The Uncanny Valley: Exploring Adversarial Robustness from a Flatness Perspective	May 27, 2024	Adversarial Robustness	CodeCode Available
Approximate Manifold Defense Against Multiple Adversarial Perturbations	Apr 5, 2020	Adversarial Robustnessimage-classification	CodeCode Available
A PAC-Bayes Analysis of Adversarial Robustness	Feb 19, 2021	Adversarial RobustnessGeneralization Bounds	CodeCode Available
Adversarial Robustness in Multi-Task Learning: Promises and Illusions	Oct 26, 2021	Adversarial RobustnessMulti-Task Learning	CodeCode Available
Image Synthesis with a Single (Robust) Classifier	Jun 6, 2019	Adversarial RobustnessImage Generation	CodeCode Available
Annealing Self-Distillation Rectification Improves Adversarial Training	May 20, 2023	Adversarial Robustness	CodeCode Available
A New Dataset Based on Images Taken by Blind People for Testing the Robustness of Image Classification Models Trained for ImageNet Categories	Jan 1, 2023	Adversarial RobustnessClassification	CodeCode Available
Neural Fingerprints for Adversarial Attack Detection	Nov 7, 2024	Adversarial AttackAdversarial Attack Detection	CodeCode Available
Computational Asymmetries in Robust Classification	Jun 25, 2023	Adversarial RobustnessClassification	CodeCode Available
Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception	Nov 12, 2021	Adversarial Robustness	CodeCode Available
Neural Representations Reveal Distinct Modes of Class Fitting in Residual Convolutional Networks	Dec 1, 2022	Adversarial RobustnessMemorization	CodeCode Available
Squeeze Training for Adversarial Robustness	May 23, 2022	Adversarial Robustness	CodeCode Available
Neuro-Symbolic Verification of Deep Neural Networks	Mar 2, 2022	Adversarial RobustnessFairness	CodeCode Available
On Adversarial Robustness: A Neural Architecture Search perspective	Jul 16, 2020	Adversarial RobustnessNeural Architecture Search	CodeCode Available
New Paradigm of Adversarial Training: Breaking Inherent Trade-Off between Accuracy and Robustness via Dummy Classes	Oct 16, 2024	Adversarial Robustness	CodeCode Available
Clustering Effect of (Linearized) Adversarial Robust Models	Nov 25, 2021	Adversarial RobustnessClustering	CodeCode Available
A Deep Dive into Adversarial Robustness in Zero-Shot Learning	Aug 17, 2020	Adversarial RobustnessBIG-bench Machine Learning	CodeCode Available
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels	May 31, 2021	Adversarial Robustness	CodeCode Available
Tighter Bounds on the Information Bottleneck with Application to Deep Learning	Feb 12, 2024	Adversarial RobustnessDeep Learning	CodeCode Available

Show:10 25 50

← PrevPage 67 of 70Next →

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