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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 791–800 of 1808 papers

Title	Date	Tasks	Status	Hype
MENLI: Robust Evaluation Metrics from Natural Language Inference	Aug 15, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
A Multi-objective Memetic Algorithm for Auto Adversarial Attack Optimization Design	Aug 15, 2022	Adversarial AttackAdversarial Defense	—Unverified	0
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
Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator	Aug 12, 2022	Adversarial AttackImage Classification	—Unverified	0
Design of secure and robust cognitive system for malware detection	Aug 3, 2022	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Multiclass ASMA vs Targeted PGD Attack in Image Segmentation	Aug 3, 2022	Adversarial AttackClassification	—Unverified	0
Look Closer to Your Enemy: Learning to Attack via Teacher-Student Mimicking	Jul 27, 2022	Adversarial AttackDomain Adaptation	CodeCode Available	0
LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity	Jul 26, 2022	Adversarial Attack	CodeCode Available	1
Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception	Jul 26, 2022	Adversarial AttackSpeaker Recognition	—Unverified	0
Versatile Weight Attack via Flipping Limited Bits	Jul 25, 2022	Adversarial AttackBackdoor 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