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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 711–720 of 1746 papers

Title	Date	Tasks	Status	Hype
Data-Driven Subsampling in the Presence of an Adversarial Actor	Jan 7, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	0
FullLoRA-AT: Efficiently Boosting the Robustness of Pretrained Vision Transformers	Jan 3, 2024	Adversarial Robustness	—Unverified	0
CausalPC: Improving the Robustness of Point Cloud Classification by Causal Effect Identification	Jan 1, 2024	Adversarial RobustnessClassification	—Unverified	0
Towards Understanding and Improving Adversarial Robustness of Vision Transformers	Jan 1, 2024	Adversarial Robustness	—Unverified	0
Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners	Jan 1, 2024	Adversarial RobustnessFew-Shot Learning	—Unverified	0
Robust Distillation via Untargeted and Targeted Intermediate Adversarial Samples	Jan 1, 2024	Adversarial RobustnessKnowledge Distillation	—Unverified	0
Random Entangled Tokens for Adversarially Robust Vision Transformer	Jan 1, 2024	Adversarial Robustness	—Unverified	0
Towards adversarial robustness verification of no-reference image-and video-quality metrics	Dec 30, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Robust Survival Analysis with Adversarial Regularization	Dec 26, 2023	Adversarial RobustnessSurvival Analysis	—Unverified	0
ARBiBench: Benchmarking Adversarial Robustness of Binarized Neural Networks	Dec 21, 2023	Adversarial RobustnessBenchmarking	—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