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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 151–160 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Training for Free!	Apr 29, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions	May 1, 2022	Adversarial AttackCombinatorial Optimization	CodeCode Available	1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition	May 7, 2021	Adversarial AttackFace Generation	CodeCode Available	1
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label	Jul 1, 2022	Adversarial AttackBackdoor Attack	CodeCode Available	1
Adversarial Attack and Defense of Structured Prediction Models	Oct 4, 2020	Adversarial AttackDependency Parsing	CodeCode Available	1
BayesOpt Adversarial Attack	May 1, 2020	Adversarial AttackBayesian Optimisation	CodeCode Available	1
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios	Feb 10, 2022	Adversarial AttackAdversarial Defense	CodeCode Available	1
BERT-ATTACK: Adversarial Attack Against BERT Using BERT	Apr 21, 2020	Adversarial Attack	CodeCode Available	1
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics	Aug 2, 2024	Adversarial AttackAdversarial Purification	CodeCode Available	1
Adversarial Ranking Attack and Defense	Feb 26, 2020	Adversarial AttackImage Retrieval	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