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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 1421–1430 of 1808 papers

Title	Date	Tasks	Status	Hype
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework	May 29, 2023	Adversarial Attack	CodeCode Available	0
Rob-GAN: Generator, Discriminator, and Adversarial Attacker	Jul 27, 2018	Adversarial AttackGenerative Adversarial Network	CodeCode Available	0
Adversarial Examples on Graph Data: Deep Insights into Attack and Defense	Mar 5, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	0
From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation	Dec 7, 2024	Adversarial AttackExplainable artificial intelligence	CodeCode Available	0
Class-Conditioned Transformation for Enhanced Robust Image Classification	Mar 27, 2023	Adversarial AttackClassification	CodeCode Available	0
Exacerbating Algorithmic Bias through Fairness Attacks	Dec 16, 2020	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	0
Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based Attacks	Jul 15, 2021	Adversarial AttackContrastive Learning	CodeCode Available	0
PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards	Aug 24, 2020	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	0
EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attacks	Jul 12, 2021	Adversarial Attack	CodeCode Available	0
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization	May 28, 2018	Adversarial AttackAdversarial Robustness	CodeCode Available	0

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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