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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 141–150 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attacks on ML Defense Models Competition	Oct 15, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow	Mar 24, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial Self-Supervised Contrastive Learning	Jun 13, 2020	Adversarial AttackContrastive Learning	CodeCode Available	1
A Review of Adversarial Attack and Defense for Classification Methods	Nov 18, 2021	Adversarial AttackClassification	CodeCode Available	1
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
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows	Jul 15, 2020	Adversarial Attack	CodeCode Available	1
Fooling the Image Dehazing Models by First Order Gradient	Mar 30, 2023	Adversarial AttackImage Dehazing	CodeCode Available	1
Attacking Video Recognition Models with Bullet-Screen Comments	Oct 29, 2021	Adversarial AttackAdversarial Attack on Video Classification	CodeCode Available	1
Augmented Lagrangian Adversarial Attacks	Nov 24, 2020	Adversarial AttackComputational Efficiency	CodeCode Available	1
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model	Nov 21, 2021	Adversarial AttackFace Recognition	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