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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 1441–1450 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Privacy-preserving Filter	Jul 25, 2020	Adversarial AttackFace Recognition	CodeCode Available	0
T-BFA: Targeted Bit-Flip Adversarial Weight Attack	Jul 24, 2020	Adversarial AttackCPU	CodeCode Available	0
Exploiting vulnerabilities of deep neural networks for privacy protection	Jul 19, 2020	Adversarial AttackQuantization	CodeCode Available	0
DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection	Jul 18, 2020	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense	Jul 17, 2020	Adversarial AttackAdversarial Defense	—Unverified	0
Accelerated Stochastic Gradient-free and Projection-free Methods	Jul 16, 2020	Adversarial Attack	CodeCode Available	0
Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack	Jul 14, 2020	Adversarial AttackCommon Sense Reasoning	—Unverified	0
Generating Adversarial Inputs Using A Black-box Differential Technique	Jul 10, 2020	Adversarial Attack	—Unverified	0
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs	Jul 8, 2020	Adversarial AttackDeep Learning	—Unverified	0
On Data Augmentation and Adversarial Risk: An Empirical Analysis	Jul 6, 2020	Adversarial AttackData Augmentation	—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