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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 261–270 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape	Apr 24, 2024	Adversarial AttackFace Swapping	CodeCode Available	1	5
Efficient Training of Robust Decision Trees Against Adversarial Examples	Dec 18, 2020	Adversarial Attack	CodeCode Available	1	5
EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability Detection	Jul 27, 2024	Adversarial AttackVulnerability Detection	CodeCode Available	1	5
AdvDrop: Adversarial Attack to DNNs by Dropping Information	Aug 20, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
An Extensive Study on Adversarial Attack against Pre-trained Models of Code	Nov 13, 2023	Adversarial Attack	CodeCode Available	1	5
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation	Mar 16, 2021	Adversarial AttackObject	CodeCode Available	1	5
An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacks	Mar 11, 2022	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	1	5
Fast and Low-Cost Genomic Foundation Models via Outlier Removal	May 1, 2025	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Exploiting the Index Gradients for Optimization-Based Jailbreaking on Large Language Models	Dec 11, 2024	Adversarial Attack	CodeCode Available	1	5
Fluent dreaming for language models	Jan 24, 2024	Adversarial AttackLanguage Modeling	CodeCode Available	1	5

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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