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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 271–280 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning	Sep 24, 2022	Adversarial Attack	CodeCode Available	1	5
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond	Jan 1, 2022	Adversarial AttackData Augmentation	CodeCode Available	1	5
epsilon-Mesh Attack: A Surface-based Adversarial Point Cloud Attack for Facial Expression Recognition	Mar 11, 2024	Adversarial AttackFacial Expression Recognition	CodeCode Available	1	5
Fluent dreaming for language models	Jan 24, 2024	Adversarial AttackLanguage Modeling	CodeCode Available	1	5
A Review of Adversarial Attack and Defense for Classification Methods	Nov 18, 2021	Adversarial AttackClassification	CodeCode Available	1	5
Are AlphaZero-like Agents Robust to Adversarial Perturbations?	Nov 7, 2022	Adversarial AttackBoard Games	CodeCode Available	1	5
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking	May 27, 2019	Adversarial AttackAutonomous Driving	CodeCode Available	1	5
Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation	Jul 14, 2023	Adversarial AttackDeep Learning	CodeCode Available	1	5
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial Attack	Oct 6, 2021	Adversarial AttackFace Swapping	CodeCode Available	1	5
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses	Nov 30, 2020	Adversarial AttackAdversarial Defense	CodeCode Available	1	5

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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