# Spectral Distribution Aware Image Generation

arXiv: https://arxiv.org/abs/2012.03110 · HTML: https://mlsanity.com/paper/2012.03110/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2020.

## Built on by (most cited first, 29 of 29)

- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [On the Frequency Bias of Generative Models](https://mlsanity.com/paper/2111.02447/) (2021)
- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis](https://mlsanity.com/paper/2105.14376/) (2021)
- [SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier Domain](https://mlsanity.com/paper/2103.03000/) (2021)
- [FrequencyLowCut Pooling -- Plug & Play against Catastrophic Overfitting](https://mlsanity.com/paper/2204.00491/) (2022)
- [CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks](https://mlsanity.com/paper/2302.02213/) (2023)
- [Seeing What Matters: Generalizable AI-generated Video Detection with Forensic-Oriented Augmentation](https://mlsanity.com/paper/2506.16802/) (2025)
- [GLaMa: Joint Spatial and Frequency Loss for General Image Inpainting](https://mlsanity.com/paper/2205.07162/) (2022)
- [Misleading Deep-Fake Detection with GAN Fingerprints](https://mlsanity.com/paper/2205.12543/) (2022)
- [When Semantic Segmentation Meets Frequency Aliasing](https://mlsanity.com/paper/2403.09065/) (2024)
- [Dream360: Diverse and Immersive Outdoor Virtual Scene Creation via Transformer-Based 360 Image Outpainting](https://mlsanity.com/paper/2401.10564/) (2024)
- [As large as it gets: Learning infinitely large Filters via Neural Implicit Functions in the Fourier Domain](https://mlsanity.com/paper/2307.10001/) (2023)
- [On the Effectiveness of Spectral Discriminators for Perceptual Quality Improvement](https://mlsanity.com/paper/2307.12027/) (2023)
- [Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context](https://mlsanity.com/paper/2311.17524/) (2023)
- [Simpler is better: spectral regularization and up-sampling techniques for variational autoencoders](https://mlsanity.com/paper/2201.07544/) (2022)
- [Beware of Aliases -- Signal Preservation is Crucial for Robust Image Restoration](https://mlsanity.com/paper/2406.07435/) (2024)
- [Towards Robust GAN-generated Image Detection: a Multi-view Completion Representation](https://mlsanity.com/paper/2306.01364/) (2023)
- [Spectrum Translation for Refinement of Image Generation (STIG) Based on Contrastive Learning and Spectral Filter Profile](https://mlsanity.com/paper/2403.05093/) (2024)
- [Guided Frequency Loss for Image Restoration](https://mlsanity.com/paper/2309.15563/) (2023)
- [Spatial Frequency Modulation for Semantic Segmentation](https://mlsanity.com/paper/2507.11893/) (2025)
- [FFR_FD: Effective and Fast Detection of DeepFakes Based on Feature Point Defects](https://mlsanity.com/paper/2107.02016/) (2021)
- [DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning](https://mlsanity.com/paper/2109.02874/) (2021)
- [Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before It's Created?](https://mlsanity.com/paper/2509.18461/) (2025)
- [Frequency Bias Matters: Diving into Robust and Generalized Deep Image Forgery Detection](https://mlsanity.com/paper/2511.19886/) (2025)
- [Images as Tables: In-Context Learning with TabPFN for Low-Data Detection of AI-Generated Images](https://mlsanity.com/paper/2606.00872/) (2026)
- [Investigating Shifts in GAN Output-Distributions](https://mlsanity.com/paper/2112.14061/) (2021)
- [Fix your downsampling ASAP! Be natively more robust via Aliasing and Spectral Artifact free Pooling](https://mlsanity.com/paper/2307.09804/) (2023)
- [Self-Creative Text-to-Object Generation using Semantic-Aware Spatial Weighting](https://mlsanity.com/paper/2605.19554/) (2026)

## Abstract (arXiv)

Recent advances in deep generative models for photo-realistic images have led to high quality visual results. Such models learn to generate data from a given training distribution such that generated images can not be easily distinguished from real images by the human eye. Yet, recent work on the detection of such fake images pointed out that they are actually easily distinguishable by artifacts in their frequency spectra. In this paper, we propose to generate images according to the frequency distribution of the real data by employing a spectral discriminator. The proposed discriminator is lightweight, modular and works stably with different commonly used GAN losses. We show that the resulting models can better generate images with realistic frequency spectra, which are thus harder to detect by this cue.