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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 121–130 of 1808 papers

Title	Date	Tasks	Status	Hype
Discriminator-Free Generative Adversarial Attack	Jul 20, 2021	Adversarial AttackDisentanglement	CodeCode Available	1
Disentangled Information Bottleneck	Dec 14, 2020	Adversarial AttackOut-of-Distribution Detection	CodeCode Available	1
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack	Dec 22, 2019	Adversarial AttackAdversarial Text	CodeCode Available	1
Distributionally Adversarial Attack	Aug 16, 2018	Adversarial Attack	CodeCode Available	1
CausalAdv: Adversarial Robustness through the Lens of Causality	Jun 11, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution	Jun 15, 2020	Adversarial Attack	CodeCode Available	1
Adversarial Training for Free!	Apr 29, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	1
Adversarial Ranking Attack and Defense	Feb 26, 2020	Adversarial AttackImage Retrieval	CodeCode Available	1
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model	Nov 21, 2021	Adversarial AttackFace Recognition	CodeCode Available	1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs	Oct 6, 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