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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
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer	Oct 14, 2021	Adversarial AttackBackdoor Attack	CodeCode Available	1
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning	Oct 13, 2021	Adversarial AttackFederated Learning	CodeCode Available	1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs	Oct 6, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial Attack	Oct 6, 2021	Adversarial AttackFace Swapping	CodeCode Available	1
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack	Sep 15, 2021	Adversarial Attackobject-detection	CodeCode Available	1
PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Models	Sep 14, 2021	Adversarial AttackText Generation	CodeCode Available	1
Multi-granularity Textual Adversarial Attack with Behavior Cloning	Sep 9, 2021	Adversarial AttackSentence	CodeCode Available	1
DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks	Aug 29, 2021	Adversarial AttackAdversarial Defense	CodeCode Available	1
AdvDrop: Adversarial Attack to DNNs by Dropping Information	Aug 20, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain	Aug 19, 2021	Adversarial AttackData Augmentation	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