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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 1301–1310 of 1808 papers

Title	Date	Tasks	Status	Hype
Perception Improvement for Free: Exploring Imperceptible Black-box Adversarial Attacks on Image Classification	Oct 30, 2020	Adversarial AttackGeneral Classification	—Unverified	0
Can the state of relevant neurons in a deep neural networks serve as indicators for detecting adversarial attacks?	Oct 29, 2020	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery Detection	Oct 29, 2020	Adversarial AttackFace Detection	CodeCode Available	1
Object Hider: Adversarial Patch Attack Against Object Detectors	Oct 28, 2020	Adversarial AttackObject	CodeCode Available	1
GreedyFool: Distortion-Aware Sparse Adversarial Attack	Oct 26, 2020	Adversarial Attack	CodeCode Available	1
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks	Oct 22, 2020	Adversarial AttackAdversarial Attack Detection	CodeCode Available	1
An Efficient Adversarial Attack for Tree Ensembles	Oct 22, 2020	Adversarial Attackvalid	CodeCode Available	1
Defense-guided Transferable Adversarial Attacks	Oct 22, 2020	Adversarial Attack	—Unverified	0
Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers	Oct 22, 2020	Adversarial AttackSemantic Similarity	—Unverified	0
Learning Black-Box Attackers with Transferable Priors and Query Feedback	Oct 21, 2020	Adversarial Attack	CodeCode Available	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