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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 441–450 of 1808 papers

Title	Date	Tasks	Status	Hype
Dynamic Adversarial Attacks on Autonomous Driving Systems	Dec 10, 2023	Adversarial AttackAutonomous Driving	CodeCode Available	0
An adversarial attack approach for eXplainable AI evaluation on deepfake detection models	Dec 8, 2023	Adversarial AttackDeepFake Detection	CodeCode Available	0
OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization	Dec 7, 2023	Adversarial AttackData Augmentation	—Unverified	0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System	Dec 6, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
ScAR: Scaling Adversarial Robustness for LiDAR Object Detection	Dec 5, 2023	3D Object DetectionAdversarial Attack	CodeCode Available	0
Realistic Scatterer Based Adversarial Attacks on SAR Image Classifiers	Dec 5, 2023	Adversarial Attack	—Unverified	0
InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models	Dec 4, 2023	Adversarial AttackLanguage Modelling	CodeCode Available	0
TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation	Dec 3, 2023	Adversarial Attackimage-classification	—Unverified	0
NeRFTAP: Enhancing Transferability of Adversarial Patches on Face Recognition using Neural Radiance Fields	Nov 29, 2023	Adversarial AttackFace Recognition	—Unverified	0
Vulnerability Analysis of Transformer-based Optical Character Recognition to Adversarial Attacks	Nov 28, 2023	Adversarial AttackOptical Character Recognition	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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