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Adversarial Attack

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1001–1010 of 1808 papers

Title	Date	Tasks	Status	Hype
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense	Nov 16, 2021	Adversarial Attack	—Unverified	0
Self-Supervised Contrastive Learning with Adversarial Perturbations for Robust Pretrained Language Models	Nov 16, 2021	Adversarial AttackContrastive Learning	—Unverified	0
BufferSearch: Generating Black-Box Adversarial Texts With Lower Queries	Nov 16, 2021	Adversarial Attacktext-classification	—Unverified	0
Robustness of Bayesian Neural Networks to White-Box Adversarial Attacks	Nov 16, 2021	Adversarial AttackBayesian Inference	—Unverified	0
Improving the robustness and accuracy of biomedical language models through adversarial training	Nov 16, 2021	Adversarial Attack	CodeCode Available	0
Towards Interpretability of Speech Pause in Dementia Detection using Adversarial Learning	Nov 14, 2021	Adversarial Attack	—Unverified	0
Sparse Adversarial Video Attacks with Spatial Transformations	Nov 10, 2021	Adversarial AttackBayesian Optimisation	CodeCode Available	1
Defense Against Explanation Manipulation	Nov 8, 2021	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models	Nov 4, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial Attack against Cross-lingual Knowledge Graph Alignment	Nov 1, 2021	Adversarial AttackEntity Alignment	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Xu et al.	Attack: PGD20	78.68	—	Unverified
2	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
3	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
4	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
5	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
6	XU-Net	Robust Accuracy	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	51.28	—	Unverified
2	multi-resolution self-ensembles	Attack: AutoAttack	47.85	—	Unverified