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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 251–260 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Differentiable JPEG: The Devil is in the Details	Sep 13, 2023	Adversarial AttackQuantization	CodeCode Available	1	5
SemanticAdv: Generating Adversarial Examples via Attribute-conditioned Image Editing	Aug 1, 2020	Adversarial AttackAttribute	CodeCode Available	1	5
DifAttack++: Query-Efficient Black-Box Adversarial Attack via Hierarchical Disentangled Feature Space in Cross-Domain	Jun 5, 2024	Adversarial AttackDisentanglement	CodeCode Available	1	5
Boosting the Adversarial Transferability of Surrogate Models with Dark Knowledge	Jun 16, 2022	Adversarial AttackFace Verification	CodeCode Available	1	5
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning	Aug 13, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models	Jul 24, 2023	Adversarial AttackAdversarial Defense	CodeCode Available	1	5
Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face Recognition	Dec 19, 2022	Adversarial AttackData Augmentation	CodeCode Available	1	5
Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems	Mar 3, 2020	Adversarial AttackAttribute	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
Feature Separation and Recalibration for Adversarial Robustness	Mar 24, 2023	Adversarial AttackAdversarial Robustness	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