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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 1481–1490 of 1808 papers

Title	Date	Tasks	Status	Hype
Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world	Mar 9, 2020	Adversarial AttackDiversity	—Unverified	0
No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks	Mar 8, 2020	Adversarial AttackLung Nodule Detection	—Unverified	0
Search Space of Adversarial Perturbations against Image Filters	Mar 5, 2020	Adversarial AttackDeep Learning	—Unverified	0
Real-time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems	Mar 4, 2020	Adversarial AttackRoom Impulse Response (RIR)	—Unverified	0
Double Backpropagation for Training Autoencoders against Adversarial Attack	Mar 4, 2020	Adversarial AttackRobust classification	—Unverified	0
Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack	Mar 3, 2020	Adversarial Attack	—Unverified	0
Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems	Mar 3, 2020	Adversarial AttackAttribute	CodeCode Available	1
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies	Mar 2, 2020	Adversarial Attack	CodeCode Available	2
Applying Tensor Decomposition to image for Robustness against Adversarial Attack	Feb 28, 2020	Adversarial AttackDeep Learning	—Unverified	0
Adversarial Ranking Attack and Defense	Feb 26, 2020	Adversarial AttackImage Retrieval	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