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 776–800 of 1746 papers

Title	Date	Tasks	Status	Score
Deep Defense: Training DNNs with Improved Adversarial Robustness	Feb 23, 2018	Adversarial Robustness	CodeCode Available	5
Analyzing and Improving the Robustness of Tabular Classifiers using Counterfactual Explanations	Dec 13, 2021	Adversarial Robustnesscounterfactual	CodeCode Available	5
Generating Adversarial Examples with Adversarial Networks	Jan 8, 2018	Adversarial Robustness	CodeCode Available	5
Hierarchical Distribution-Aware Testing of Deep Learning	May 17, 2022	Adversarial RobustnessData Compression	CodeCode Available	5
DeepBern-Nets: Taming the Complexity of Certifying Neural Networks using Bernstein Polynomial Activations and Precise Bound Propagation	May 22, 2023	Adversarial RobustnessFairness	CodeCode Available	5
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More	Jan 1, 2021	Adversarial RobustnessClassification	CodeCode Available	5
Boosting Adversarial Training via Fisher-Rao Norm-based Regularization	Mar 26, 2024	Adversarial Robustness	CodeCode Available	5
Deep anytime-valid hypothesis testing	Oct 30, 2023	Adversarial RobustnessTwo-sample testing	CodeCode Available	5
Learning Energy-Based Models With Adversarial Training	Dec 11, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available	5
Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspective	Aug 20, 2022	Adversarial Robustnessimage-classification	CodeCode Available	5
SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation	Dec 12, 2022	Adversarial RobustnessDomain Adaptation	CodeCode Available	5
Adversarial Robustness Analysis of Vision-Language Models in Medical Image Segmentation	May 5, 2025	Adversarial AttackAdversarial Robustness	CodeCode Available	5
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels	May 31, 2021	Adversarial Robustness	CodeCode Available	5
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models	Jun 20, 2023	Adversarial RobustnessEthics	—Unverified	0
Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors	Jun 21, 2024	Adversarial DefenseAdversarial Robustness	—Unverified	0
Analyzing Adversarial Robustness of Deep Neural Networks in Pixel Space: a Semantic Perspective	Jun 18, 2021	Adversarial Robustness	—Unverified	0
Deadwooding: Robust Global Pruning for Deep Neural Networks	Feb 10, 2022	Adversarial Robustness	—Unverified	0
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks	May 13, 2019	Adversarial RobustnessIntrusion Detection	—Unverified	0
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data	Jan 15, 2021	Adversarial RobustnessContinual Learning	—Unverified	0
DataFreeShield: Defending Adversarial Attacks without Training Data	Jun 21, 2024	Adversarial RobustnessDataset Generation	—Unverified	0
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection	Feb 25, 2024	Adversarial RobustnessCyber Attack Detection	—Unverified	0
Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustness	Jun 28, 2024	Adversarial Robustness	—Unverified	0
DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation	Feb 16, 2024	Adversarial RobustnessDomain Adaptation	—Unverified	0
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness	Mar 1, 2021	Adversarial RobustnessObject Recognition	—Unverified	0
Adversarial Robustness Across Representation Spaces	Dec 1, 2020	Adversarial Robustnessimage-classification	—Unverified	0

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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