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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 1631–1640 of 1808 papers

Title	Date	Tasks	Status	Hype
Learning Visually-Grounded Semantics from Contrastive Adversarial Samples	Jun 27, 2018	Adversarial AttackImage Captioning	CodeCode Available	0
Learn To Pay Attention	Apr 6, 2018	Adversarial AttackGeneral Classification	CodeCode Available	0
Structured Adversarial Attack: Towards General Implementation and Better Interpretability	Aug 5, 2018	Adversarial Attack	CodeCode Available	0
Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems	Jul 11, 2024	Adversarial AttackFace Recognition	CodeCode Available	0
Adversarial sample generation and training using geometric masks for accurate and resilient license plate character recognition	Oct 25, 2023	Adversarial AttackLicense Plate Detection	CodeCode Available	0
Leveraging Information Consistency in Frequency and Spatial Domain for Adversarial Attacks	Aug 22, 2024	Adversarial Attack	CodeCode Available	0
Adversarial Robustness for Visual Grounding of Multimodal Large Language Models	May 16, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	0
REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions	Jun 18, 2020	Adversarial Attack	CodeCode Available	0
LiDAttack: Robust Black-box Attack on LiDAR-based Object Detection	Nov 4, 2024	Adversarial Attackobject-detection	CodeCode Available	0
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations	Oct 17, 2022	Adversarial AttackAdversarial Robustness	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