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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 161–170 of 1808 papers

Title	Date	Tasks	Status	Hype
CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval	Apr 18, 2022	Adversarial AttackAdversarial Defense	CodeCode Available	1
Certifying LLM Safety against Adversarial Prompting	Sep 6, 2023	Adversarial AttackLanguage Modelling	CodeCode Available	1
Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems	Aug 18, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes	May 23, 2021	Adversarial AttackFace Swapping	CodeCode Available	1
Composite Adversarial Attacks	Dec 10, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data	Jun 2, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial Examples for Semantic Segmentation and Object Detection	Mar 24, 2017	Adversarial Attackimage-classification	CodeCode Available	1
3D Gaussian Splat Vulnerabilities	May 30, 2025	3DGSAdversarial Attack	CodeCode Available	1
Adversarial Examples in Deep Learning for Multivariate Time Series Regression	Sep 24, 2020	Adversarial AttackDeep Learning	CodeCode Available	1
Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting	Dec 11, 2024	Adversarial AttackTime Series	CodeCode Available	1

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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