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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 1671–1680 of 1808 papers

Title	Date	Tasks	Status	Hype
Different Spectral Representations in Optimized Artificial Neural Networks and Brains	Aug 22, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Switching Transferable Gradient Directions for Query-Efficient Black-Box Adversarial Attacks	Sep 15, 2020	Adversarial Attack	CodeCode Available	0
Towards Safe Synthetic Image Generation On the Web: A Multimodal Robust NSFW Defense and Million Scale Dataset	Apr 16, 2025	Adversarial AttackImage Generation	CodeCode Available	0
Differentiable Adversarial Attacks for Marked Temporal Point Processes	Jan 17, 2025	Adversarial AttackPoint Processes	CodeCode Available	0
MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks	Aug 6, 2019	Adversarial AttackAdversarial Attack Detection	CodeCode Available	0
TabAttackBench: A Benchmark for Adversarial Attacks on Tabular Data	May 27, 2025	Adversarial Attack	CodeCode Available	0
Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial Attacks	Jul 1, 2020	Adversarial Attack	CodeCode Available	0
Robust Decision Trees Against Adversarial Examples	Feb 27, 2019	Adversarial AttackAdversarial Defense	CodeCode Available	0
With Friends Like These, Who Needs Adversaries?	Jul 11, 2018	Adversarial AttackClassification	CodeCode Available	0
Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks	Nov 3, 2020	Adversarial AttackMachine Translation	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