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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 191–200 of 1808 papers

Title	Date	Tasks	Status	Hype
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models	Jul 24, 2023	Adversarial AttackAdversarial Defense	CodeCode Available	1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition	May 7, 2021	Adversarial AttackFace Generation	CodeCode Available	1
Fusing Event-based and RGB camera for Robust Object Detection in Adverse Conditions	Mar 30, 2022	3D Object DetectionAdversarial Attack	CodeCode Available	1
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model	Jan 11, 2024	Adversarial Attack	CodeCode Available	1
Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem	Jun 21, 2021	Adversarial Attack	CodeCode Available	1
Adversarial Magnification to Deceive Deepfake Detection through Super Resolution	Jul 2, 2024	Adversarial AttackDeepFake Detection	CodeCode Available	1
Geometric Adversarial Attacks and Defenses on 3D Point Clouds	Dec 10, 2020	Adversarial AttackAdversarial Defense	CodeCode Available	1
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model	Nov 21, 2021	Adversarial AttackFace Recognition	CodeCode Available	1
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows	Jul 15, 2020	Adversarial Attack	CodeCode Available	1
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

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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