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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 131–140 of 1808 papers

Title	Date	Tasks	Status	Hype
InvisibiliTee: Angle-agnostic Cloaking from Person-Tracking Systems with a Tee	Aug 15, 2022	Adversarial AttackHuman Detection	CodeCode Available	1
MENLI: Robust Evaluation Metrics from Natural Language Inference	Aug 15, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks	Aug 12, 2022	Adversarial Attack	CodeCode Available	1
LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity	Jul 26, 2022	Adversarial Attack	CodeCode Available	1
SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness	Jul 25, 2022	Adversarial AttackSegmentation	CodeCode Available	1
Prior-Guided Adversarial Initialization for Fast Adversarial Training	Jul 18, 2022	Adversarial AttackAdversarial Attack on Video Classification	CodeCode Available	1
CARBEN: Composite Adversarial Robustness Benchmark	Jul 16, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Perturbation Inactivation Based Adversarial Defense for Face Recognition	Jul 13, 2022	Adversarial AttackAdversarial Defense	CodeCode Available	1
Frequency Domain Model Augmentation for Adversarial Attack	Jul 12, 2022	Adversarial AttackDiversity	CodeCode Available	1
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label	Jul 1, 2022	Adversarial AttackBackdoor Attack	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