# Misleading Deep-Fake Detection with GAN Fingerprints

arXiv: https://arxiv.org/abs/2205.12543 · HTML: https://mlsanity.com/paper/2205.12543/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 39 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 22 of 22)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues](https://mlsanity.com/paper/2007.09355/) (2020)
- [Leveraging Frequency Analysis for Deep Fake Image Recognition](https://mlsanity.com/paper/2003.08685/) (2020)
- [Detecting and Simulating Artifacts in GAN Fake Images](https://mlsanity.com/paper/1907.06515/) (2019)
- [Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions](https://mlsanity.com/paper/2003.01826/) (2020)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Do GANs leave artificial fingerprints?](https://mlsanity.com/paper/1812.11842/) (2018)
- [The Cramer Distance as a Solution to Biased Wasserstein Gradients](https://mlsanity.com/paper/1705.10743/) (2017)
- [Evading Deepfake-Image Detectors with White- and Black-Box Attacks](https://mlsanity.com/paper/2004.00622/) (2020)
- [Unmasking DeepFakes with simple Features](https://mlsanity.com/paper/1911.00686/) (2019)
- [Adversarial Threats to DeepFake Detection: A Practical Perspective](https://mlsanity.com/paper/2011.09957/) (2020)
- [GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection](https://mlsanity.com/paper/1911.05351/) (2019)
- [FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction](https://mlsanity.com/paper/2006.07533/) (2020)
- [Spectral Distribution Aware Image Generation](https://mlsanity.com/paper/2012.03110/) (2020)
- [SpoC: Spoofing Camera Fingerprints](https://mlsanity.com/paper/1911.12069/) (2019)
- [Preliminary Forensics Analysis of DeepFake Images](https://mlsanity.com/paper/2004.12626/) (2020)

## Built on by (most cited first, 11 of 11)

- [An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape](https://mlsanity.com/paper/2404.16212/) (2024)
- [GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer](https://mlsanity.com/paper/2307.07036/) (2023)
- [RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors](https://mlsanity.com/paper/2506.03988/) (2025)
- [SPRINT: Robust Model Attribution of Generated Images via Secret Pixel Reconstruction](https://mlsanity.com/paper/2508.05691/) (2025)
- [FakeChain: Exposing Shallow Cues in Multi-Step Deepfake Detection](https://mlsanity.com/paper/2509.16602/) (2025)
- [Human vs. AI: A Novel Benchmark and a Comparative Study on the Detection of Generated Images and the Impact of Prompts](https://mlsanity.com/paper/2412.09715/) (2024)
- [Smudged Fingerprints: A Systematic Evaluation of the Robustness of AI Image Fingerprints](https://mlsanity.com/paper/2512.11771/) (2025)
- [FIND: A Simple yet Effective Baseline for Diffusion-Generated Image Detection](https://mlsanity.com/paper/2603.14220/) (2026)
- [Untraceable DeepFakes via Traceable Fingerprint Elimination](https://mlsanity.com/paper/2508.03067/) (2025)
- [Chroma Clues: Leveraging Color Statistics to Detect Synthetic Images](https://mlsanity.com/paper/2606.02224/) (2026)
- [Can Pixels Alone Reveal Image Origin? Minimax Limits and Learnable Interfaces for Passive Provenance](https://mlsanity.com/paper/2609.30997/) (2026)

## Abstract (arXiv)

Generative adversarial networks (GANs) have made remarkable progress in synthesizing realistic-looking images that effectively outsmart even humans. Although several detection methods can recognize these deep fakes by checking for image artifacts from the generation process, multiple counterattacks have demonstrated their limitations. These attacks, however, still require certain conditions to hold, such as interacting with the detection method or adjusting the GAN directly. In this paper, we introduce a novel class of simple counterattacks that overcomes these limitations. In particular, we show that an adversary can remove indicative artifacts, the GAN fingerprint, directly from the frequency spectrum of a generated image. We explore different realizations of this removal, ranging from filtering high frequencies to more nuanced frequency-peak cleansing. We evaluate the performance of our attack with different detection methods, GAN architectures, and datasets. Our results show that an adversary can often remove GAN fingerprints and thus evade the detection of generated images.