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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 1561–1570 of 1808 papers

Title	Date	Tasks	Status	Hype
Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensemble	Feb 12, 2024	Adversarial AttackClassification	CodeCode Available	0
Random Transformation of Image Brightness for Adversarial Attack	Jan 12, 2021	Adversarial AttackData Augmentation	CodeCode Available	0
Boosting Adversarial Attacks with Momentum	Oct 17, 2017	Adversarial Attack	CodeCode Available	0
SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking	Oct 19, 2019	Adversarial AttackObject Tracking	CodeCode Available	0
Block-Sparse Adversarial Attack to Fool Transformer-Based Text Classifiers	Mar 11, 2022	Adversarial AttackSentence	CodeCode Available	0
InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models	Dec 4, 2023	Adversarial AttackLanguage Modelling	CodeCode Available	0
Black-Box Adversarial Attack with Transferable Model-based Embedding	Nov 17, 2019	Adversarial Attack	CodeCode Available	0
Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models	Oct 17, 2021	Adversarial AttackGraph Neural Network	CodeCode Available	0
Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs	Aug 21, 2023	Adversarial AttackLink Prediction	CodeCode Available	0
READ: Improving Relation Extraction from an ADversarial Perspective	Apr 2, 2024	Adversarial AttackRelation	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