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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 721–730 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables	May 10, 2019	Adversarial AttackImage Captioning	CodeCode Available	0	5
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks	Nov 27, 2018	Adversarial Attack	CodeCode Available	0	5
Class-Conditioned Transformation for Enhanced Robust Image Classification	Mar 27, 2023	Adversarial AttackClassification	CodeCode Available	0	5
Exacerbating Algorithmic Bias through Fairness Attacks	Dec 16, 2020	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	0	5
Physics-constrained Attack against Convolution-based Human Motion Prediction	Jun 21, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
Rethinking Independent Cross-Entropy Loss For Graph-Structured Data	May 24, 2024	Adversarial AttackNode Classification	CodeCode Available	0	5
Excess Capacity and Backdoor Poisoning	Sep 2, 2021	Adversarial AttackBackdoor Attack	CodeCode Available	0	5
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness	May 31, 2019	Adversarial AttackAdversarial Attack Detection	CodeCode Available	0	5
A Theoretical View of Linear Backpropagation and Its Convergence	Dec 21, 2021	Adversarial Attack	CodeCode Available	0	5
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components	Dec 1, 2019	Adversarial AttackClassification	CodeCode Available	0	5

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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