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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 601–610 of 1808 papers

Title	Date	Tasks	Status	Hype
Embodied Laser Attack:Leveraging Scene Priors to Achieve Agent-based Robust Non-contact Attacks	Dec 15, 2023	Adversarial AttackAutonomous Driving	—Unverified	0
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique	Nov 14, 2019	Adversarial Attackimage-classification	—Unverified	0
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization	Apr 11, 2022	Adversarial AttackObject	—Unverified	0
Adversarial Attack and Defense on Point Sets	Feb 28, 2019	Adversarial Attack	—Unverified	0
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks	Jul 31, 2023	Adversarial AttackIntrusion Detection	—Unverified	0
Adversarial Eigen Attack on Black-Box Models	Aug 27, 2020	Adversarial Attack	—Unverified	0
Efficient universal shuffle attack for visual object tracking	Mar 14, 2022	Adversarial AttackComputational Efficiency	—Unverified	0
Activation Learning by Local Competitions	Sep 26, 2022	Adversarial AttackAnomaly Detection	—Unverified	0
AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack	May 6, 2020	Adversarial AttackClassification	—Unverified	0
A Non-monotonic Smooth Activation Function	Oct 16, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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