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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 551–560 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective	Jan 1, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0	5
EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attacks	Jul 12, 2021	Adversarial Attack	CodeCode Available	0	5
Adversarial Attack via Dual-Stage Network Erosion	Jan 1, 2022	Adversarial Attack	CodeCode Available	0	5
Exacerbating Algorithmic Bias through Fairness Attacks	Dec 16, 2020	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	0	5
An adversarial attack approach for eXplainable AI evaluation on deepfake detection models	Dec 8, 2023	Adversarial AttackDeepFake Detection	CodeCode Available	0	5
An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework	Apr 27, 2022	Adversarial Attack	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
An Evasion Attack against Stacked Capsule Autoencoder	Oct 14, 2020	Adversarial Attackimage-classification	CodeCode Available	0	5
Evaluating and Understanding the Robustness of Adversarial Logit Pairing	Jul 26, 2018	Adversarial Attack	CodeCode Available	0	5
Evaluating the Robustness of Adversarial Defenses in Malware Detection Systems	May 14, 2025	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	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