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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 281–290 of 1808 papers

Title	Date	Tasks	Status	Hype
CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security Data	Mar 12, 2025	Adversarial AttackMalware Analysis	CodeCode Available	1
Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification	Dec 21, 2020	Adversarial AttackBackdoor Attack	CodeCode Available	1
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation	Oct 12, 2022	Adversarial Attack	CodeCode Available	1
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC	Mar 18, 2022	Adversarial Attack	CodeCode Available	1
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain	Aug 19, 2021	Adversarial AttackData Augmentation	CodeCode Available	1
Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks	Aug 12, 2022	Adversarial Attack	CodeCode Available	1
3D Adversarial Attacks Beyond Point Cloud	Apr 25, 2021	Adversarial Attack	CodeCode Available	1
An Extensive Study on Adversarial Attack against Pre-trained Models of Code	Nov 13, 2023	Adversarial Attack	CodeCode Available	1
Discriminator-Free Generative Adversarial Attack	Jul 20, 2021	Adversarial AttackDisentanglement	CodeCode Available	1
Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity	Mar 10, 2022	Adversarial AttackSemantic Similarity	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