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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 1581–1590 of 1808 papers

Title	Date	Tasks	Status	Hype
Defending Against Adversarial Examples by Regularized Deep Embedding	Sep 25, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0
Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness	Sep 25, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0
Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection	Sep 25, 2019	Adversarial Attack	—Unverified	0
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack	Sep 24, 2019	Adversarial AttackAdversarial Robustness	CodeCode Available	0
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection	Sep 20, 2019	Adversarial AttackMalware Detection	—Unverified	0
Propagated Perturbation of Adversarial Attack for well-known CNNs: Empirical Study and its Explanation	Sep 19, 2019	Adversarial AttackExplainable Artificial Intelligence (XAI)	—Unverified	0
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks	Sep 19, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review	Sep 17, 2019	Adversarial Attack	CodeCode Available	2
Natural Language Adversarial Defense through Synonym Encoding	Sep 15, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	0
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms	Sep 15, 2019	Adversarial AttackAdversarial Robustness	—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