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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 1681–1690 of 1808 papers

Title	Date	Tasks	Status	Hype
Mimic and Fool: A Task Agnostic Adversarial Attack	Jun 11, 2019	Adversarial AttackImage Captioning	CodeCode Available	0
Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion Models	Sep 12, 2024	Adversarial AttackAdversarial Purification	CodeCode Available	0
Using BERT Encoding to Tackle the Mad-lib Attack in SMS Spam Detection	Jul 13, 2021	Adversarial AttackSpam detection	CodeCode Available	0
BERTops: Studying BERT Representations under a Topological Lens	May 2, 2022	Adversarial Attack	CodeCode Available	0
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack	Jul 3, 2019	Adversarial Attack	CodeCode Available	0
TAPE: Assessing Few-shot Russian Language Understanding	Oct 23, 2022	Adversarial AttackAdversarial Text	CodeCode Available	0
AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy Gradient	Oct 13, 2022	Adversarial Attack	CodeCode Available	0
Detecting Adversarial Perturbations in Multi-Task Perception	Mar 2, 2022	Adversarial AttackDepth Estimation	CodeCode Available	0
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework	Oct 4, 2022	Adversarial AttackClustering	CodeCode Available	0
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification	Dec 13, 2023	Adversarial AttackEntity Typing	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