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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 191–200 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Data-free Universal Adversarial Perturbation with Pseudo-semantic Prior	Feb 28, 2025	Adversarial Attack	CodeCode Available	1	5
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction	Jan 16, 2022	Adversarial AttackCombinatorial Optimization	CodeCode Available	1	5
Contextualized Perturbation for Textual Adversarial Attack	Sep 16, 2020	Adversarial AttackLanguage Modeling	CodeCode Available	1	5
On the Multi-modal Vulnerability of Diffusion Models	Feb 2, 2024	Adversarial AttackImage Generation	CodeCode Available	1	5
Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem	Jun 21, 2021	Adversarial Attack	CodeCode Available	1	5
Adversarial Magnification to Deceive Deepfake Detection through Super Resolution	Jul 2, 2024	Adversarial AttackDeepFake Detection	CodeCode Available	1	5
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks	May 28, 2019	Adversarial AttackVocal Bursts Intensity Prediction	CodeCode Available	1	5
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model	Nov 21, 2021	Adversarial AttackFace Recognition	CodeCode Available	1	5
Benchmarking Adversarial Robustness on Image Classification	Jun 1, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes	May 23, 2021	Adversarial AttackFace Swapping	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