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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 221–230 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art	Dec 23, 2021	Adversarial AttackMalware Detection	CodeCode Available	1
Object Hider: Adversarial Patch Attack Against Object Detectors	Oct 28, 2020	Adversarial AttackObject	CodeCode Available	1
On Improving Adversarial Transferability of Vision Transformers	Jun 8, 2021	Adversarial Attack	CodeCode Available	1
Adversarial Self-Supervised Contrastive Learning	Jun 13, 2020	Adversarial AttackContrastive Learning	CodeCode Available	1
Online Adversarial Attacks	Mar 2, 2021	Adversarial Attack	CodeCode Available	1
On the Adversarial Robustness of Camera-based 3D Object Detection	Jan 25, 2023	3D Object DetectionAdversarial Attack	CodeCode Available	1
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability	Aug 5, 2023	Adversarial Attack	CodeCode Available	1
Adversarial Training for Free!	Apr 29, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1
Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models	Sep 14, 2022	Adversarial AttackLanguage Modelling	CodeCode Available	1
An Extensive Study on Adversarial Attack against Pre-trained Models of Code	Nov 13, 2023	Adversarial Attack	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