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 1276–1300 of 1746 papers

Title	Date	Tasks	Status
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data	Jun 15, 2020	Adversarial RobustnessData Augmentation	—Unverified
Improving Adversarial Robustness via Feature Pattern Consistency Constraint	Jun 13, 2024	Adversarial Robustnessfeature selection	—Unverified
Improving Adversarial Robustness via Phase and Amplitude-aware Prompting	Feb 6, 2025	Adversarial Robustness	—Unverified
Improving Adversarial Robustness with Hypersphere Embedding and Angular-based Regularizations	Mar 15, 2023	Adversarial Robustness	—Unverified
Improving Adversarial Transferability of Vision-Language Pre-training Models through Collaborative Multimodal Interaction	Mar 16, 2024	Adversarial RobustnessImage-text Retrieval	—Unverified
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning	Mar 11, 2024	Adversarial RobustnessDeep Learning	—Unverified
Improving Fast Adversarial Training via Self-Knowledge Guidance	Sep 26, 2024	Adversarial RobustnessAttribute	—Unverified
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness	Aug 16, 2023	Adversarial RobustnessSubgraph Counting	CodeCode Available
Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing	Nov 20, 2019	Adversarial Robustness	CodeCode Available
An Analysis of Robustness of Non-Lipschitz Networks	Oct 13, 2020	Adversarial AttackAdversarial Defense	CodeCode Available
On the Privacy Effect of Data Enhancement via the Lens of Memorization	Aug 17, 2022	Adversarial RobustnessData Augmentation	CodeCode Available
Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation	Dec 16, 2024	Adversarial RobustnessMixture-of-Experts	CodeCode Available
Carefully Blending Adversarial Training, Purification, and Aggregation Improves Adversarial Robustness	May 25, 2023	Adversarial DefenseAdversarial Purification	CodeCode Available
Careful What You Wish For: on the Extraction of Adversarially Trained Models	Jul 21, 2022	Adversarial Robustness	CodeCode Available
FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods	Mar 11, 2025	Adversarial RobustnessFace Detection	CodeCode Available
FaiR-N: Fair and Robust Neural Networks for Structured Data	Oct 13, 2020	Adversarial RobustnessAttribute	CodeCode Available
Towards adversarial robustness verification of no-reference image-and video-quality metrics	Dec 30, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available
On the Relationship between Skill Neurons and Robustness in Prompt Tuning	Sep 21, 2023	Adversarial Robustness	CodeCode Available
Testing Robustness Against Unforeseen Adversaries	Aug 21, 2019	Adversarial DefenseAdversarial Robustness	CodeCode Available
Fake It Until You Break It: On the Adversarial Robustness of AI-generated Image Detectors	Oct 2, 2024	Adversarial RobustnessMisinformation	CodeCode Available
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms	Dec 1, 2020	Adversarial RobustnessQuantization	CodeCode Available
Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams	Nov 19, 2020	Adversarial Robustness	CodeCode Available
Fast Adversarial Training with Smooth Convergence	Aug 24, 2023	Adversarial Robustness	CodeCode Available
Towards adversarial robustness with 01 loss neural networks	Aug 20, 2020	Adversarial RobustnessBinary Classification	CodeCode Available
Expressive Losses for Verified Robustness via Convex Combinations	May 23, 2023	Adversarial Robustness	CodeCode Available

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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