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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 1031–1040 of 1746 papers

Title	Date	Tasks	Status	Hype	Score
Classifier-independent Lower-Bounds for Adversarial Robustness	Jun 17, 2020	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Analyzing Adversarial Robustness of Deep Neural Networks in Pixel Space: a Semantic Perspective	Jun 18, 2021	Adversarial Robustness	—Unverified	0	0
Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks	Apr 4, 2024	Adversarial DefenseAdversarial Robustness	—Unverified	0	0
Towards Achieving Adversarial Robustness Beyond Perceptual Limits	Sep 29, 2021	Adversarial DefenseAdversarial Robustness	—Unverified	0	0
Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend	May 18, 2023	Adversarial AttackAdversarial Defense	—Unverified	0	0
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks	May 13, 2019	Adversarial RobustnessIntrusion Detection	—Unverified	0	0
Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives	Feb 17, 2025	Adversarial Robustness	—Unverified	0	0
Mind the box: l_1-APGD for sparse adversarial attacks on image classifiers	Mar 1, 2021	Adversarial Robustness	—Unverified	0	0
Min-Max Optimization under Delays	Jul 13, 2023	Adversarial RobustnessStochastic Optimization	—Unverified	0	0
MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer	May 2, 2022	Adversarial Robustnessimage-classification	—Unverified	0	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