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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 601–610 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensemble	Feb 12, 2024	Adversarial AttackClassification	CodeCode Available	0	5
Generate synthetic samples from tabular data	Sep 12, 2022	Adversarial Attack	CodeCode Available	0	5
Defense-friendly Images in Adversarial Attacks: Dataset and Metrics for Perturbation Difficulty	Nov 5, 2020	Adversarial AttackBenchmarking	CodeCode Available	0	5
A Classification-Guided Approach for Adversarial Attacks against Neural Machine Translation	Aug 29, 2023	Adversarial AttackMachine Translation	CodeCode Available	0	5
Excess Capacity and Backdoor Poisoning	Sep 2, 2021	Adversarial AttackBackdoor Attack	CodeCode Available	0	5
Explainable Graph Neural Networks Under Fire	Jun 10, 2024	Adversarial Attack	CodeCode Available	0	5
Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial Texts	Dec 1, 2021	Adversarial Attacktext-classification	CodeCode Available	0	5
Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Training	Mar 2, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
Graph Neural Network Explanations are Fragile	Jun 5, 2024	Adversarial AttackGraph Neural Network	CodeCode Available	0	5
Curls & Whey: Boosting Black-Box Adversarial Attacks	Apr 2, 2019	Adversarial AttackDiversity	CodeCode Available	0	5

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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