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 1–10 of 1808 papers

Title	Date	Tasks	Status	Hype
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms	Jul 19, 2022	Adversarial AttackMultivariate Time Series Forecasting	CodeCode Available	5
Universal and Transferable Adversarial Attacks on Aligned Language Models	Jul 27, 2023	Adversarial AttackIngenuity	CodeCode Available	4
Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment	May 27, 2025	Adversarial AttackClustering	CodeCode Available	2
SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders	Jan 29, 2025	Adversarial AttackDenoising	CodeCode Available	2
Adversarial Attacks and Defenses on Text-to-Image Diffusion Models: A Survey	Jul 10, 2024	Adversarial AttackImage Generation	CodeCode Available	2
On Discrete Prompt Optimization for Diffusion Models	Jun 27, 2024	Adversarial AttackPrompt Engineering	CodeCode Available	2
RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models	Jun 16, 2024	Adversarial AttackBenchmarking	CodeCode Available	2
DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection	May 16, 2024	Adversarial AttackFace Recognition	CodeCode Available	2
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack	Apr 2, 2024	Adversarial AttackText Detection	CodeCode Available	2
Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving	Mar 26, 2024	Adversarial AttackAutonomous Driving	CodeCode Available	2

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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