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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 901–910 of 1808 papers

Title	Date	Tasks	Status	Hype
Input-specific Attention Subnetworks for Adversarial Detection	Mar 23, 2022	Adversarial Attack	—Unverified	0
Exploring High-Order Structure for Robust Graph Structure Learning	Mar 22, 2022	Adversarial AttackGraph structure learning	—Unverified	0
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement	Mar 21, 2022	Adversarial Attack	—Unverified	0
Efficient Neural Network Analysis with Sum-of-Infeasibilities	Mar 19, 2022	Adversarial AttackEfficient Neural Network	CodeCode Available	2
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense	Mar 19, 2022	Adversarial Attack	CodeCode Available	0
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC	Mar 18, 2022	Adversarial Attack	CodeCode Available	1
RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression	Mar 18, 2022	Adversarial AttackClassification	—Unverified	0
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack	Mar 18, 2022	Adversarial AttackNetwork Pruning	—Unverified	0
DTA: Physical Camouflage Attacks using Differentiable Transformation Network	Mar 18, 2022	Adversarial AttackObject	—Unverified	0
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training	Mar 14, 2022	Adversarial AttackClassification	—Unverified	0

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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