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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 891–900 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments	Dec 11, 2022	Adversarial AttackAdversarial Defense	—Unverified	0	0
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment	Aug 19, 2022	Adversarial AttackGrammatical Error Correction	—Unverified	0	0
Hiding Backdoors within Event Sequence Data via Poisoning Attacks	Aug 20, 2023	Adversarial Attack	—Unverified	0	0
CE-based white-box adversarial attacks will not work using super-fitting	May 4, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0	0
Adversarial Profiles: Detecting Out-Distribution & Adversarial Samples in Pre-trained CNNs	Nov 18, 2020	Adversarial Attack	—Unverified	0	0
Adversarial Attack on Sentiment Classification	Aug 1, 2019	Adversarial AttackClassification	—Unverified	0	0
A Deep Genetic Programming based Methodology for Art Media Classification Robust to Adversarial Perturbations	Oct 3, 2020	Adversarial AttackDeep Learning	—Unverified	0	0
A Black-Box Attack on Optical Character Recognition Systems	Aug 30, 2022	Adversarial AttackBinarization	—Unverified	0	0
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection	Apr 29, 2021	Adversarial AttackGeneral Classification	—Unverified	0	0
GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization	Aug 20, 2024	Adversarial AttackGraph Neural Network	—Unverified	0	0

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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