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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 1661–1670 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based Vision	Oct 6, 2021	Adversarial AttackEvent-based vision	CodeCode Available	0
Susceptibility of Adversarial Attack on Medical Image Segmentation Models	Jan 20, 2024	Adversarial AttackImage Segmentation	CodeCode Available	0
RoBIC: A benchmark suite for assessing classifiers robustness	Feb 10, 2021	Adversarial Attack	CodeCode Available	0
Disrupting Adversarial Transferability in Deep Neural Networks	Aug 27, 2021	Adversarial AttackFeature Correlation	CodeCode Available	0
A New Perspective on Stabilizing GANs training: Direct Adversarial Training	Aug 19, 2020	Adversarial AttackImage Generation	CodeCode Available	0
Malafide: a novel adversarial convolutive noise attack against deepfake and spoofing detection systems	Jun 13, 2023	Adversarial AttackFace Swapping	CodeCode Available	0
Unfooling Perturbation-Based Post Hoc Explainers	May 29, 2022	Adversarial AttackAnomaly Detection	CodeCode Available	0
Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology Imaging	Jan 4, 2024	Adversarial AttackDomain Adaptation	CodeCode Available	0
SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural Networks	Jun 4, 2024	Adversarial Attack	CodeCode Available	0
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models	Aug 14, 2017	Adversarial AttackAdversarial Defense	CodeCode Available	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