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 151–175 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial Vulnerability of Randomized Ensembles	Jun 14, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial vulnerability of powerful near out-of-distribution detection	Jan 18, 2022	Adversarial RobustnessOut-of-Distribution Detection	CodeCode Available	1	5
A Light Recipe to Train Robust Vision Transformers	Sep 15, 2022	Adversarial RobustnessData Augmentation	CodeCode Available	1	5
RobFR: Benchmarking Adversarial Robustness on Face Recognition	Jul 8, 2020	Adversarial RobustnessBenchmarking	CodeCode Available	1	5
Bispectral Neural Networks	Sep 7, 2022	Adversarial RobustnessRepresentation Learning	CodeCode Available	1	5
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning	Aug 13, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication	Dec 13, 2024	Adversarial Robustness	CodeCode Available	1	5
Demystify Transformers & Convolutions in Modern Image Deep Networks	Nov 10, 2022	Adversarial RobustnessImage Deep Networks	CodeCode Available	1	5
Adversarial Robustness of Deep Convolutional Candlestick Learner	May 29, 2020	Adversarial Robustness	CodeCode Available	1	5
On the Adversarial Robustness of Vision Transformers	Mar 29, 2021	Adversarial Robustness	CodeCode Available	1	5
Efficient Image-to-Image Diffusion Classifier for Adversarial Robustness	Aug 16, 2024	Adversarial Robustnessimage-classification	CodeCode Available	1	5
Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion Models	Apr 16, 2024	Adversarial DefenseAdversarial Robustness	CodeCode Available	1	5
Enhancing Adversarial Robustness for Deep Metric Learning	Mar 2, 2022	Adversarial RobustnessMetric Learning	CodeCode Available	1	5
Enhancing adversarial robustness in Natural Language Inference using explanations	Sep 11, 2024	Adversarial RobustnessNatural Language Inference	CodeCode Available	1	5
Enhancing Adversarial Robustness via Test-time Transformation Ensembling	Jul 29, 2021	Adversarial Robustness	CodeCode Available	1	5
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder	May 6, 2020	Adversarial RobustnessDecoder	CodeCode Available	1	5
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks	May 19, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion	Mar 29, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Explainability-Aware One Point Attack for Point Cloud Neural Networks	Oct 8, 2021	3D Object RecognitionAdversarial Robustness	CodeCode Available	1	5
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness	Mar 20, 2023	Adversarial RobustnessMalware Detection	CodeCode Available	1	5
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow	Mar 24, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks	Oct 7, 2021	Adversarial Robustness	CodeCode Available	1	5
Adversarial Attacks on Graph Classifiers via Bayesian Optimisation	Dec 1, 2021	Adversarial RobustnessBayesian Optimisation	CodeCode Available	1	5
Adversarial Robustness Against the Union of Multiple Threat Models	Jan 1, 2020	Adversarial Robustness	CodeCode Available	1	5
Adversarial Robustness on In- and Out-Distribution Improves Explainability	Mar 20, 2020	Adversarial Robustnessimage-classification	CodeCode Available	1	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