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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 871–880 of 1808 papers

Title	Date	Tasks	Status	Hype
An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework	Apr 27, 2022	Adversarial Attack	CodeCode Available	0
Boosting Adversarial Transferability of MLP-Mixer	Apr 26, 2022	Adversarial Attack	—Unverified	0
Restricted Black-box Adversarial Attack Against DeepFake Face Swapping	Apr 26, 2022	Adversarial AttackFace Reconstruction	—Unverified	0
Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks	Apr 26, 2022	Adversarial AttackAdversarial Defense	CodeCode Available	1
Mixed Strategies for Security Games with General Defending Requirements	Apr 26, 2022	Adversarial Attack	—Unverified	0
Improving Deep Learning Model Robustness Against Adversarial Attack by Increasing the Network Capacity	Apr 24, 2022	Adversarial AttackDeep Learning	—Unverified	0
Smart App Attack: Hacking Deep Learning Models in Android Apps	Apr 23, 2022	Adversarial AttackBinary Classification	CodeCode Available	1
Enhancing the Transferability via Feature-Momentum Adversarial Attack	Apr 22, 2022	Adversarial Attack	—Unverified	0
How Sampling Impacts the Robustness of Stochastic Neural Networks	Apr 22, 2022	Adversarial Attack	—Unverified	0
A Mask-Based Adversarial Defense Scheme	Apr 21, 2022	Adversarial AttackAdversarial Defense	—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