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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 161–170 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain	Aug 19, 2021	Adversarial AttackData Augmentation	CodeCode Available	1	5
DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks	Aug 29, 2021	Adversarial AttackAdversarial Defense	CodeCode Available	1	5
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model	Nov 21, 2021	Adversarial AttackFace Recognition	CodeCode Available	1	5
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability	Aug 5, 2023	Adversarial Attack	CodeCode Available	1	5
Efficient Training of Robust Decision Trees Against Adversarial Examples	Dec 18, 2020	Adversarial Attack	CodeCode Available	1	5
Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness	Aug 8, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Examples for Semantic Segmentation and Object Detection	Mar 24, 2017	Adversarial Attackimage-classification	CodeCode Available	1	5
3D Gaussian Splat Vulnerabilities	May 30, 2025	3DGSAdversarial Attack	CodeCode Available	1	5
Adversarial Examples in Deep Learning for Multivariate Time Series Regression	Sep 24, 2020	Adversarial AttackDeep Learning	CodeCode Available	1	5
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks	May 28, 2019	Adversarial AttackVocal Bursts Intensity Prediction	CodeCode Available	1	5

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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