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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 291–300 of 1808 papers

Title	Date	Tasks	Status	Hype
Towards Feature Space Adversarial Attack	Apr 26, 2020	Adversarial AttackAdversarial Attack Detection	CodeCode Available	1
BERT-ATTACK: Adversarial Attack Against BERT Using BERT	Apr 21, 2020	Adversarial Attack	CodeCode Available	1
Adversarial Attack on Deep Learning-Based Splice Localization	Apr 17, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking	Apr 8, 2020	Adversarial AttackPerson Re-Identification	CodeCode Available	1
Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible Noises	Mar 21, 2020	Adversarial Attack	CodeCode Available	1
Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior	Mar 17, 2020	Adversarial AttackVideo Classification	CodeCode Available	1
Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems	Mar 3, 2020	Adversarial AttackAttribute	CodeCode Available	1
Adversarial Ranking Attack and Defense	Feb 26, 2020	Adversarial AttackImage Retrieval	CodeCode Available	1
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization	Feb 12, 2020	Adversarial AttackNeural Architecture Search	CodeCode Available	1
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks	Feb 10, 2020	Adversarial AttackDeblurring	CodeCode Available	1

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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