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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 721–730 of 1808 papers

Title	Date	Tasks	Status	Hype
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning	Nov 15, 2022	Adversarial Attackreinforcement-learning	—Unverified	0
MORA: Improving Ensemble Robustness Evaluation with Model-Reweighing Attack	Nov 15, 2022	Adversarial Attack	CodeCode Available	1
Generating Textual Adversaries with Minimal Perturbation	Nov 12, 2022	Adversarial Attack	CodeCode Available	0
Robust Smart Home Face Recognition under Starving Federated Data	Nov 10, 2022	Adversarial AttackFace Recognition	CodeCode Available	0
Preserving Semantics in Textual Adversarial Attacks	Nov 8, 2022	Adversarial AttackSentence	CodeCode Available	1
Are AlphaZero-like Agents Robust to Adversarial Perturbations?	Nov 7, 2022	Adversarial AttackBoard Games	CodeCode Available	1
Contrastive Weighted Learning for Near-Infrared Gaze Estimation	Nov 6, 2022	Adversarial AttackContrastive Learning	—Unverified	0
Logits are predictive of network type	Nov 4, 2022	Adversarial AttackVocal Bursts Type Prediction	CodeCode Available	0
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain	Nov 1, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Rethinking Image Restoration for Object Detection	Nov 1, 2022	Adversarial AttackDomain Adaptation	CodeCode Available	1

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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