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 526–550 of 1746 papers

Title	Date	Tasks	Status	Hype
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness	Sep 28, 2023	Adversarial Robustness	—Unverified	0
Projected Randomized Smoothing for Certified Adversarial Robustness	Sep 25, 2023	Adversarial Robustness	CodeCode Available	0
Improving Robustness of Deep Convolutional Neural Networks via Multiresolution Learning	Sep 24, 2023	Adversarial Robustness	—Unverified	0
RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias	Sep 23, 2023	Adversarial Robustness	—Unverified	0
VIC-KD: Variance-Invariance-Covariance Knowledge Distillation to Make Keyword Spotting More Robust Against Adversarial Attacks	Sep 22, 2023	Adversarial RobustnessKeyword Spotting	—Unverified	0
On the Relationship between Skill Neurons and Robustness in Prompt Tuning	Sep 21, 2023	Adversarial Robustness	CodeCode Available	0
How Robust is Google's Bard to Adversarial Image Attacks?	Sep 21, 2023	Adversarial RobustnessChatbot	CodeCode Available	1
Language Guided Adversarial Purification	Sep 19, 2023	Adversarial DefenseAdversarial Purification	CodeCode Available	0
Evaluating Adversarial Robustness with Expected Viable Performance	Sep 18, 2023	Adversarial Robustness	—Unverified	0
DAD++: Improved Data-free Test Time Adversarial Defense	Sep 10, 2023	Adversarial DefenseAdversarial Robustness	CodeCode Available	0
Exploring Robust Features for Improving Adversarial Robustness	Sep 9, 2023	Adversarial RobustnessDisentanglement	—Unverified	0
Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing	Sep 8, 2023	Adversarial Robustnessregression	CodeCode Available	0
Adversarially Robust Learning with Optimal Transport Regularized Divergences	Sep 7, 2023	Adversarial RobustnessDeep Learning	CodeCode Available	0
J-Guard: Journalism Guided Adversarially Robust Detection of AI-generated News	Sep 6, 2023	Adversarial RobustnessArticles	CodeCode Available	0
RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems	Sep 5, 2023	Adversarial RobustnessQuantization	—Unverified	0
Robust Principles: Architectural Design Principles for Adversarially Robust CNNs	Aug 30, 2023	Adversarial Robustness	CodeCode Available	1
Advancing Adversarial Robustness Through Adversarial Logit Update	Aug 29, 2023	Adversarial PurificationAdversarial Robustness	—Unverified	0
Prediction without Preclusion: Recourse Verification with Reachable Sets	Aug 24, 2023	Adversarial RobustnessPrediction	CodeCode Available	0
Fast Adversarial Training with Smooth Convergence	Aug 24, 2023	Adversarial Robustness	CodeCode Available	0
Don't Look into the Sun: Adversarial Solarization Attacks on Image Classifiers	Aug 24, 2023	Adversarial Robustnessimage-classification	CodeCode Available	0
Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging	Aug 22, 2023	Adversarial RobustnessData Augmentation	CodeCode Available	1
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models	Aug 21, 2023	Adversarial RobustnessBenchmarking	—Unverified	0
On the Adversarial Robustness of Multi-Modal Foundation Models	Aug 21, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	1
HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds	Aug 20, 2023	Adversarial Robustness	CodeCode Available	1
Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting	Aug 20, 2023	Adversarial Robustness	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