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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 611–620 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Expanding Scope: Adapting English Adversarial Attacks to Chinese	Jun 8, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
Adversarial and Clean Data Are Not Twins	Apr 17, 2017	Adversarial Attack	CodeCode Available	0	5
CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability	Oct 22, 2023	Adversarial Attack	CodeCode Available	0	5
DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms in Vision Transformers	Feb 4, 2024	Adversarial AttackGPU	CodeCode Available	0	5
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables	May 10, 2019	Adversarial AttackImage Captioning	CodeCode Available	0	5
Explainable and Safe Reinforcement Learning for Autonomous Air Mobility	Nov 24, 2022	Adversarial AttackDeep Reinforcement Learning	CodeCode Available	0	5
Detecting Adversarial Perturbations in Multi-Task Perception	Mar 2, 2022	Adversarial AttackDepth Estimation	CodeCode Available	0	5
Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion Models	Sep 12, 2024	Adversarial AttackAdversarial Purification	CodeCode Available	0	5
Evaluating the Validity of Word-level Adversarial Attacks with Large Language Models	Aug 15, 2024	Adversarial AttackLanguage Modeling	CodeCode Available	0	5
Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Training	Mar 2, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5

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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