# MANI-Pure: Magnitude-Adaptive Noise Injection for Adversarial Purification

arXiv: https://arxiv.org/abs/2509.25082 · HTML: https://mlsanity.com/paper/2509.25082/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models.
- Builds on 30 of its 39 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Placed in

- Diffusion models / [Robustness, privacy and adversarial aspects](https://mlsanity.com/diffusion/robustness-privacy-and-adversarial-aspects/)

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [Diffusion Models for Adversarial Purification](https://mlsanity.com/paper/2205.07460/) (2022)
- [Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models](https://mlsanity.com/paper/1805.06605/) (2018)
- [A Fourier Perspective on Model Robustness in Computer Vision](https://mlsanity.com/paper/1906.08988/) (2019)
- [Better Diffusion Models Further Improve Adversarial Training](https://mlsanity.com/paper/2302.04638/) (2023)
- [Understanding Zero-Shot Adversarial Robustness for Large-Scale Models](https://mlsanity.com/paper/2212.07016/) (2022)
- [Guided Diffusion Model for Adversarial Purification](https://mlsanity.com/paper/2205.14969/) (2022)
- [Robust Evaluation of Diffusion-Based Adversarial Purification](https://mlsanity.com/paper/2303.09051/) (2023)
- [Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models](https://mlsanity.com/paper/2005.13525/) (2020)
- [HoliTom: Holistic Token Merging for Fast Video Large Language Models](https://mlsanity.com/paper/2505.21334/) (2025)
- [Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors](https://mlsanity.com/paper/2006.06356/) (2020)
- [MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification](https://mlsanity.com/paper/2406.05927/) (2024)
- [Adversarial Training on Purification (AToP): Advancing Both Robustness and Generalization](https://mlsanity.com/paper/2401.16352/) (2024)
- [Instant Adversarial Purification with Adversarial Consistency Distillation](https://mlsanity.com/paper/2408.17064/) (2024)
- [CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot Classification](https://mlsanity.com/paper/2502.18176/) (2025)
- [Overcoming False Illusions in Real-World Face Restoration with Multi-Modal Guided Diffusion Model](https://mlsanity.com/paper/2410.04161/) (2024)
- [Diffusion-based Adversarial Purification from the Perspective of the Frequency Domain](https://mlsanity.com/paper/2505.01267/) (2025)
- [Towards Adversarial Robustness via Debiased High-Confidence Logit Alignment](https://mlsanity.com/paper/2408.06079/) (2024)
- [Diffusion-based Cumulative Adversarial Purification for Vision Language Models](https://mlsanity.com/paper/2506.03933/) (2025)
- [Sample-Specific Noise Injection For Diffusion-Based Adversarial Purification](https://mlsanity.com/paper/2506.06027/) (2025)

## Built on by (most cited first, 1 of 1)

- [Adaptive Causal Alignment for High-Confidence Adversarial Training](https://mlsanity.com/paper/2606.03925/) (2026)

## Abstract (arXiv)

Adversarial purification with diffusion models has emerged as a promising defense strategy, but existing methods typically rely on uniform noise injection, which indiscriminately perturbs all frequencies, corrupting semantic structures and undermining robustness. Our empirical study reveals that adversarial perturbations are not uniformly distributed: they are predominantly concentrated in high-frequency regions, with heterogeneous magnitude intensity patterns that vary across frequencies and attack types. Motivated by this observation, we introduce MANI-Pure, a magnitude-adaptive purification framework that leverages the magnitude spectrum of inputs to guide the purification process. Instead of injecting homogeneous noise, MANI-Pure adaptively applies heterogeneous, frequency-targeted noise, effectively suppressing adversarial perturbations in fragile high-frequency, low-magnitude bands while preserving semantically critical low-frequency content. Extensive experiments on CIFAR-10 and ImageNet-1K validate the effectiveness of MANI-Pure. It narrows the clean accuracy gap to within 0.59 of the original classifier, while boosting robust accuracy by 2.15, and achieves the top-1 robust accuracy on the RobustBench leaderboard, surpassing the previous state-of-the-art method.