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 726–750 of 1746 papers

Title	Date	Tasks	Status	Score
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness	Sep 21, 2020	Adversarial RobustnessDiversity	CodeCode Available	5
Deterministic Gaussian Averaged Neural Networks	Jun 10, 2020	Adversarial Robustnessregression	CodeCode Available	5
Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow	Oct 26, 2023	Adversarial RobustnessMotion Detection	CodeCode Available	5
Annealing Self-Distillation Rectification Improves Adversarial Training	May 20, 2023	Adversarial Robustness	CodeCode Available	5
RDI: An adversarial robustness evaluation metric for deep neural networks based on model statistical features	Apr 16, 2025	Adversarial RobustnessComputational Efficiency	CodeCode Available	5
Dense Hopfield Networks in the Teacher-Student Setting	Jan 8, 2024	Adversarial Robustness	CodeCode Available	5
A New Dataset Based on Images Taken by Blind People for Testing the Robustness of Image Classification Models Trained for ImageNet Categories	Jan 1, 2023	Adversarial RobustnessClassification	CodeCode Available	5
Demystifying the Adversarial Robustness of Random Transformation Defenses	Jun 18, 2022	Adversarial RobustnessAutonomous Vehicles	CodeCode Available	5
DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization	Dec 8, 2024	Adversarial RobustnessMemorization	CodeCode Available	5
Adversarial Robustness Certification for Bayesian Neural Networks	Jun 23, 2023	Adversarial RobustnessCollision Avoidance	CodeCode Available	5
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial Pruning	Jun 15, 2022	Adversarial RobustnessQuantization	CodeCode Available	5
On Adversarial Robustness: A Neural Architecture Search perspective	Jul 16, 2020	Adversarial RobustnessNeural Architecture Search	CodeCode Available	5
Testing Robustness Against Unforeseen Adversaries	Aug 21, 2019	Adversarial DefenseAdversarial Robustness	CodeCode Available	5
Adversarial Robustness by Design through Analog Computing and Synthetic Gradients	Jan 6, 2021	Adversarial RobustnessBinarization	CodeCode Available	5
GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models	Apr 19, 2023	Adversarial Robustness	CodeCode Available	5
Efficiently Training Low-Curvature Neural Networks	Jun 14, 2022	Adversarial Robustness	CodeCode Available	5
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness	May 25, 2019	Adversarial Robustness	CodeCode Available	5
Hierarchical Distribution-Aware Testing of Deep Learning	May 17, 2022	Adversarial RobustnessData Compression	CodeCode Available	5
An Empirical Study of Accuracy-Robustness Tradeoff and Training Efficiency in Self-Supervised Learning	Jan 7, 2025	Adversarial RobustnessContrastive Learning	CodeCode Available	5
Give me a hint: Can LLMs take a hint to solve math problems?	Oct 8, 2024	Adversarial RobustnessMath	CodeCode Available	5
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More	Jan 1, 2021	Adversarial RobustnessClassification	CodeCode Available	5
Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial Robustness	Mar 30, 2023	Adversarial Robustness	CodeCode Available	5
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach	Oct 30, 2021	Adversarial Robustness	CodeCode Available	5
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers	Aug 1, 2023	Adversarial Robustnessimage-classification	CodeCode Available	5
Global-Local Regularization Via Distributional Robustness	Mar 1, 2022	Adversarial RobustnessDomain Adaptation	CodeCode Available	5

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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