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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 1101–1110 of 1808 papers

Title	Date	Tasks	Status	Hype
Discriminator-Free Generative Adversarial Attack	Jul 20, 2021	Adversarial AttackDisentanglement	CodeCode Available	1
Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition	Jul 19, 2021	Adversarial AttackFace Recognition	—Unverified	0
Feature-Filter: Detecting Adversarial Examples through Filtering off Recessive Features	Jul 19, 2021	Adversarial Attack	—Unverified	0
Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based Attacks	Jul 15, 2021	Adversarial AttackContrastive Learning	CodeCode Available	0
Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks	Jul 15, 2021	Adversarial Attack	—Unverified	0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning	Jul 14, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
Using BERT Encoding to Tackle the Mad-lib Attack in SMS Spam Detection	Jul 13, 2021	Adversarial AttackSpam detection	CodeCode Available	0
EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attacks	Jul 12, 2021	Adversarial Attack	CodeCode Available	0
Noise-based cyberattacks generating fake P300 waves in brain–computer interfaces	Jul 10, 2021	Adversarial AttackCyber Attack Investigation	CodeCode Available	0
Learning to Detect Adversarial Examples Based on Class Scores	Jul 9, 2021	Adversarial AttackAdversarial Attack Detection	—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