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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 181–190 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
A Survey On Universal Adversarial Attack	Mar 2, 2021	Adversarial AttackSurvey	CodeCode Available	1	5
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink	Mar 11, 2021	Adversarial Attack	CodeCode Available	1	5
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks	May 24, 2022	Adversarial Attack	CodeCode Available	1	5
Adversarial Learning for Robust Deep Clustering	Dec 1, 2020	Adversarial AttackClustering	CodeCode Available	1	5
A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space	Dec 2, 2021	Adversarial AttackAdversarial Robustness	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
R&R: Metric-guided Adversarial Sentence Generation	Apr 17, 2021	Adversarial AttackGeneral Classification	CodeCode Available	1	5
Attacking Recommender Systems with Augmented User Profiles	May 17, 2020	Adversarial AttackGenerative Adversarial Network	CodeCode Available	1	5
Adversarial Attack on Deep Learning-Based Splice Localization	Apr 17, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Magnification to Deceive Deepfake Detection through Super Resolution	Jul 2, 2024	Adversarial AttackDeepFake Detection	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