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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 21–30 of 1808 papers

Title	Date	Tasks	Status	Hype
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack	Apr 2, 2024	Adversarial AttackText Detection	CodeCode Available	2
Ignore Previous Prompt: Attack Techniques For Language Models	Nov 17, 2022	Adversarial AttackAdversarial Text	CodeCode Available	2
Efficient Neural Network Analysis with Sum-of-Infeasibilities	Mar 19, 2022	Adversarial AttackEfficient Neural Network	CodeCode Available	2
RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models	Jun 16, 2024	Adversarial AttackBenchmarking	CodeCode Available	2
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models	Mar 4, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	2
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art	Dec 23, 2021	Adversarial AttackMalware Detection	CodeCode Available	1
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot Navigation	Jun 19, 2025	Adversarial AttackRobot Navigation	CodeCode Available	1
Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem	Jun 21, 2021	Adversarial Attack	CodeCode Available	1
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models	May 16, 2025	Adversarial AttackAdversarial Defense	CodeCode Available	1
Adversarial Attack on Large Scale Graph	Sep 8, 2020	Adversarial Attack	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