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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 501–510 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Fashion-Guided Adversarial Attack on Person Segmentation	Apr 17, 2021	Adversarial AttackHuman Instance Segmentation	CodeCode Available	0	5
Certified Defenses against Adversarial Examples	Jan 29, 2018	Adversarial AttackAdversarial Defense	CodeCode Available	0	5
Adversarial Examples in Modern Machine Learning: A Review	Nov 13, 2019	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	0	5
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables	May 10, 2019	Adversarial AttackImage Captioning	CodeCode Available	0	5
Active Subspace of Neural Networks: Structural Analysis and Universal Attacks	Oct 29, 2019	Adversarial AttackUncertainty Quantification	CodeCode Available	0	5
Patch augmentation: Towards efficient decision boundaries for neural networks	Nov 8, 2019	Adversarial Attack	CodeCode Available	0	5
Exacerbating Algorithmic Bias through Fairness Attacks	Dec 16, 2020	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	0	5
Excess Capacity and Backdoor Poisoning	Sep 2, 2021	Adversarial AttackBackdoor Attack	CodeCode Available	0	5
A principled approach for generating adversarial images under non-smooth dissimilarity metrics	Aug 5, 2019	Adversarial Attack	CodeCode Available	0	5
A practical approach to evaluating the adversarial distance for machine learning classifiers	Sep 5, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	0	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