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 801–825 of 1746 papers

Title	Date	Tasks	Status
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation	Aug 8, 2023	Adversarial RobustnessKnowledge Distillation	CodeCode Available
Fixed Inter-Neuron Covariability Induces Adversarial Robustness	Aug 7, 2023	Adversarial Robustness	—Unverified
Unsupervised Adversarial Detection without Extra Model: Training Loss Should Change	Aug 7, 2023	Adversarial Robustness	CodeCode Available
Exploring the Physical World Adversarial Robustness of Vehicle Detection	Aug 7, 2023	Adversarial AttackAdversarial Robustness	—Unverified
RobustMQ: Benchmarking Robustness of Quantized Models	Aug 4, 2023	Adversarial RobustnessBenchmarking	—Unverified
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers	Aug 1, 2023	Adversarial Robustnessimage-classification	CodeCode Available
Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning	Aug 1, 2023	Adversarial Robustness	—Unverified
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness	Aug 1, 2023	Adversarial AttackAdversarial Robustness	—Unverified
Robust Linear Regression: Phase-Transitions and Precise Tradeoffs for General Norms	Aug 1, 2023	Adversarial Robustnessregression	—Unverified
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives	Jul 31, 2023	Adversarial RobustnessFairness	—Unverified
Characterizing Data Point Vulnerability via Average-Case Robustness	Jul 26, 2023	Adversarial RobustnessMulti-class Classification	CodeCode Available
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix	Jul 25, 2023	Adversarial AttackAdversarial Robustness	—Unverified
Exploring the Sharpened Cosine Similarity	Jul 25, 2023	Adversarial Robustnessimage-classification	—Unverified
Homophily-Driven Sanitation View for Robust Graph Contrastive Learning	Jul 24, 2023	Adversarial RobustnessContrastive Learning	—Unverified
A Holistic Assessment of the Reliability of Machine Learning Systems	Jul 20, 2023	Adversarial RobustnessOut-of-Distribution Detection	—Unverified
Omnipotent Adversarial Training in the Wild	Jul 14, 2023	Adversarial Robustness	CodeCode Available
Min-Max Optimization under Delays	Jul 13, 2023	Adversarial RobustnessStochastic Optimization	—Unverified
Function-Space Regularization for Deep Bayesian Classification	Jul 12, 2023	Adversarial RobustnessClassification	—Unverified
A unifying framework for differentially private quantum algorithms	Jul 10, 2023	Adversarial Robustness	—Unverified
A Theoretical Perspective on Subnetwork Contributions to Adversarial Robustness	Jul 7, 2023	Adversarial Robustness	—Unverified
Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness	Jul 5, 2023	Adversarial RobustnessLearning Theory	—Unverified
Kernels, Data & Physics	Jul 5, 2023	Adversarial RobustnessInductive Bias	—Unverified
On the Adversarial Robustness of Generative Autoencoders in the Latent Space	Jul 5, 2023	Adversarial Robustnesscompressed sensing	—Unverified
The Importance of Robust Features in Mitigating Catastrophic Forgetting	Jun 29, 2023	Adversarial RobustnessContinual Learning	—Unverified
Advancing Adversarial Training by Injecting Booster Signal	Jun 27, 2023	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