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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 321–330 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
FDA: Feature Disruptive Attack	Sep 10, 2019	Adversarial Attackimage-classification	CodeCode Available	0	5
Accelerated Stochastic Gradient-free and Projection-free Methods	Jul 16, 2020	Adversarial Attack	CodeCode Available	0	5
Fast Inference of Removal-Based Node Influence	Mar 13, 2024	Adversarial Attackcounterfactual	CodeCode Available	0	5
Fashion-Guided Adversarial Attack on Person Segmentation	Apr 17, 2021	Adversarial AttackHuman Instance Segmentation	CodeCode Available	0	5
Fast Adversarial CNN-based Perturbation Attack of No-Reference Image Quality Metrics	Apr 11, 2023	Adversarial AttackNo-Reference Image Quality Assessment	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
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions	Apr 14, 2020	Adversarial AttackEmotion Classification	CodeCode Available	0	5
Feature Space Perturbations Yield More Transferable Adversarial Examples	Jun 1, 2019	Adversarial Attack	CodeCode Available	0	5
Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective	Jan 1, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds	Dec 1, 2019	Adversarial AttackClassify 3D Point Clouds	CodeCode Available	0	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