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 951–975 of 1746 papers

Title	Date	Tasks	Status
Invariance vs Robustness of Neural Networks	Sep 25, 2019	Adversarial Robustnessimage-classification	—Unverified
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications	Oct 29, 2019	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE	Oct 10, 2023	Adversarial RobustnessDensity Estimation	—Unverified
Adversarial Contrastive Distillation with Adaptive Denoising	Feb 17, 2023	Adversarial RobustnessDenoising	—Unverified
Investigating the Impact of Quantization on Adversarial Robustness	Apr 8, 2024	Adversarial RobustnessQuantization	—Unverified
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning	May 22, 2020	Adversarial AttackAdversarial Robustness	—Unverified
Anticipatory Thinking Challenges in Open Worlds: Risk Management	Jun 22, 2023	Adversarial RobustnessAutonomous Vehicles	—Unverified
A Novel Noise Injection-based Training Scheme for Better Model Robustness	Feb 17, 2023	Adversarial RobustnessComputational Efficiency	—Unverified
Adversarial Bone Length Attack on Action Recognition	Sep 13, 2021	Action RecognitionAdversarial Robustness	—Unverified
Is Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks?	Dec 2, 2021	Adversarial Robustness	—Unverified
Is current research on adversarial robustness addressing the right problem?	Jul 31, 2022	Adversarial Robustness	—Unverified
A Note on Implementation Errors in Recent Adaptive Attacks Against Multi-Resolution Self-Ensembles	Jan 24, 2025	Adversarial Robustness	—Unverified
Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models	Jul 3, 2025	Adversarial RobustnessAll	—Unverified
A Non-monotonic Smooth Activation Function	Oct 16, 2023	Adversarial AttackAdversarial Robustness	—Unverified
Iterative Adversarial Attack on Image-guided Story Ending Generation	May 16, 2023	Adversarial AttackAdversarial Robustness	—Unverified
It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness	Mar 17, 2023	Adversarial RobustnessAll	—Unverified
Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks	Aug 26, 2021	Adversarial Robustness	—Unverified
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks	Jun 5, 2018	Adversarial AttackAdversarial Robustness	—Unverified
The Pros and Cons of Adversarial Robustness	Dec 18, 2023	Adversarial Robustness	—Unverified
Kernels, Data & Physics	Jul 5, 2023	Adversarial RobustnessInductive Bias	—Unverified
The robust way to stack and bag: the local Lipschitz way	Jun 1, 2022	Adversarial Robustness	—Unverified
Achieving Adversarial Robustness via Sparsity	Sep 11, 2020	Adversarial RobustnessNetwork Pruning	—Unverified
k-Mixup Regularization for Deep Learning via Optimal Transport	Sep 29, 2021	Adversarial RobustnessDeep Learning	—Unverified
Knowledge-Augmented Reasoning for EUAIA Compliance and Adversarial Robustness of LLMs	Oct 4, 2024	Adversarial Robustness	—Unverified
Knowledge-guided Semantic Computing Network	Sep 29, 2018	Adversarial RobustnessObject Recognition	—Unverified

Show:10 25 50

← PrevPage 39 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