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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 281–290 of 1808 papers

Title	Date	Tasks	Status	Hype
Interpolation between Residual and Non-Residual Networks	Jun 10, 2020	Adversarial Attackimage-classification	CodeCode Available	1
Pick-Object-Attack: Type-Specific Adversarial Attack for Object Detection	Jun 5, 2020	Adversarial AttackImage Captioning	CodeCode Available	1
Benchmarking Adversarial Robustness on Image Classification	Jun 1, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack	Jun 1, 2020	Adversarial AttackBinarization	CodeCode Available	1
On Intrinsic Dataset Properties for Adversarial Machine Learning	May 19, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Defending Your Voice: Adversarial Attack on Voice Conversion	May 18, 2020	Adversarial AttackVoice Conversion	CodeCode Available	1
Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspective	May 18, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Attacking Recommender Systems with Augmented User Profiles	May 17, 2020	Adversarial AttackGenerative Adversarial Network	CodeCode Available	1
BayesOpt Adversarial Attack	May 1, 2020	Adversarial AttackBayesian Optimisation	CodeCode Available	1
Sign Bits Are All You Need for Black-Box Attacks	May 1, 2020	Adversarial AttackAll	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