# Backdoor Attack through Frequency Domain

arXiv: https://arxiv.org/abs/2111.10991 · HTML: https://mlsanity.com/paper/2111.10991/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 75 references that are in the map.
- First posted on arXiv 22 Nov 2021.

## Builds on (most cited first, 30 of 40)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019)
- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning](https://mlsanity.com/paper/1712.05526/) (2017)
- [Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs](https://mlsanity.com/paper/1802.10026/) (2018)
- [Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks](https://mlsanity.com/paper/1805.12185/) (2018)
- [Spectral Signatures in Backdoor Attacks](https://mlsanity.com/paper/1811.00636/) (2018)
- [STRIP: A Defence Against Trojan Attacks on Deep Neural Networks](https://mlsanity.com/paper/1902.06531/) (2019)
- [Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering](https://mlsanity.com/paper/1811.03728/) (2018)
- [Hidden Trigger Backdoor Attacks](https://mlsanity.com/paper/1910.00033/) (2019)
- [Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks](https://mlsanity.com/paper/1901.06523/) (2019)
- [Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks](https://mlsanity.com/paper/2007.02343/) (2020)
- [Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks](https://mlsanity.com/paper/2101.05930/) (2021)
- [Input-Aware Dynamic Backdoor Attack](https://mlsanity.com/paper/2010.08138/) (2020)
- [High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks](https://mlsanity.com/paper/1905.13545/) (2019)
- [A Fourier Perspective on Model Robustness in Computer Vision](https://mlsanity.com/paper/1906.08988/) (2019)
- [A new Backdoor Attack in CNNs by training set corruption without label poisoning](https://mlsanity.com/paper/1902.11237/) (2019)
- [A Note on the Inception Score](https://mlsanity.com/paper/1801.01973/) (2018)
- [Training behavior of deep neural network in frequency domain](https://mlsanity.com/paper/1807.01251/) (2018)
- [Clean-Label Backdoor Attacks on Video Recognition Models](https://mlsanity.com/paper/2003.03030/) (2020)

## Built on by (most cited first, 18 of 18)

- [MM-BD: Post-Training Detection of Backdoor Attacks with Arbitrary Backdoor Pattern Types Using a Maximum Margin Statistic](https://mlsanity.com/paper/2205.06900/) (2022)
- [Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger](https://mlsanity.com/paper/2302.14677/) (2023)
- [FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2312.04432/) (2023)
- [Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation](https://mlsanity.com/paper/2207.10825/) (2022)
- [Robust Anti-Backdoor Instruction Tuning in LVLMs](https://mlsanity.com/paper/2506.05401/) (2025)
- [ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks](https://mlsanity.com/paper/2210.00108/) (2022)
- [Don't FREAK Out: A Frequency-Inspired Approach to Detecting Backdoor Poisoned Samples in DNNs](https://mlsanity.com/paper/2303.13211/) (2023)
- [Look, Listen, and Attack: Backdoor Attacks Against Video Action Recognition](https://mlsanity.com/paper/2301.00986/) (2023)
- [Spy-Watermark: Robust Invisible Watermarking for Backdoor Attack](https://mlsanity.com/paper/2401.02031/) (2024)
- [Towards Physical World Backdoor Attacks against Skeleton Action Recognition](https://mlsanity.com/paper/2408.08671/) (2024)
- [Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations](https://mlsanity.com/paper/2503.14895/) (2025)
- [Invisible Backdoor Attacks Using Data Poisoning in the Frequency Domain](https://mlsanity.com/paper/2207.04209/) (2022)
- [Protecting against simultaneous data poisoning attacks](https://mlsanity.com/paper/2408.13221/) (2024)
- [AS-FIBA: Adaptive Selective Frequency-Injection for Backdoor Attack on Deep Face Restoration](https://mlsanity.com/paper/2403.06430/) (2024)
- [Stealthy and Robust Backdoor Attack against 3D Point Clouds through Additional Point Features](https://mlsanity.com/paper/2412.07511/) (2024)
- [Backdoor Attacks on Prompt-Driven Video Segmentation Foundation Models](https://mlsanity.com/paper/2512.22046/) (2025)
- [Invisible Clean-Label Backdoor Attacks for Generative Data Augmentation](https://mlsanity.com/paper/2602.03316/) (2026)
- [Beyond Small Patches: Black-Box Detection and Purification of Diverse Backdoor Triggers](https://mlsanity.com/paper/2609.03139/) (2026)

## Abstract (arXiv)

Backdoor attacks have been shown to be a serious threat against deep learning systems such as biometric authentication and autonomous driving. An effective backdoor attack could enforce the model misbehave under certain predefined conditions, i.e., triggers, but behave normally otherwise. However, the triggers of existing attacks are directly injected in the pixel space, which tend to be detectable by existing defenses and visually identifiable at both training and inference stages. In this paper, we propose a new backdoor attack FTROJAN through trojaning the frequency domain. The key intuition is that triggering perturbations in the frequency domain correspond to small pixel-wise perturbations dispersed across the entire image, breaking the underlying assumptions of existing defenses and making the poisoning images visually indistinguishable from clean ones. We evaluate FTROJAN in several datasets and tasks showing that it achieves a high attack success rate without significantly degrading the prediction accuracy on benign inputs. Moreover, the poisoning images are nearly invisible and retain high perceptual quality. We also evaluate FTROJAN against state-of-the-art defenses as well as several adaptive defenses that are designed on the frequency domain. The results show that FTROJAN can robustly elude or significantly degenerate the performance of these defenses.