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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 671–680 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex	May 29, 2019	Adversarial AttackBayesian Inference	CodeCode Available	0	5
Exploiting vulnerabilities of deep neural networks for privacy protection	Jul 19, 2020	Adversarial AttackQuantization	CodeCode Available	0	5
NOMARO: Defending against Adversarial Attacks by NOMA-Inspired Reconstruction Operation	Dec 14, 2021	Adversarial AttackAdversarial Defense	CodeCode Available	0	5
Dynamics-aware Adversarial Attack of 3D Sparse Convolution Network	Dec 17, 2021	3D Classification3D Semantic Segmentation	CodeCode Available	0	5
A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks	Dec 17, 2020	Adversarial AttackDecision Making	CodeCode Available	0	5
Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective	Jan 1, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
Dynamic Transformers Provide a False Sense of Efficiency	May 20, 2023	Adversarial Attack	CodeCode Available	0	5
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples	Sep 13, 2017	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
Excess Capacity and Backdoor Poisoning	Sep 2, 2021	Adversarial AttackBackdoor Attack	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

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Acc	90.1	90.1(1)	Community Verified
2	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
3	ResNet20	Test Acc	89.5	90.1(1)	Community Verified
4	Xu et al.	Attack: PGD20	78.68	—	Unverified
5	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
6	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
7	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
8	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
9	XU-Net	Robust Accuracy	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Acc	80.4	—	Community Verified
2	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	51.28	—	Unverified
3	multi-resolution self-ensembles	Attack: AutoAttack	47.85	—	Unverified