# Fake-image detection with Robust Hashing

arXiv: https://arxiv.org/abs/2102.01313 · HTML: https://mlsanity.com/paper/2102.01313/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 16 of 16)

- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Face2Face: Real-time Face Capture and Reenactment of RGB Videos](https://mlsanity.com/paper/2007.14808/) (2020)
- [Exposing Deep Fakes Using Inconsistent Head Poses](https://mlsanity.com/paper/1811.00661/) (2018)
- [Detecting and Simulating Artifacts in GAN Fake Images](https://mlsanity.com/paper/1907.06515/) (2019)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Learning Rich Features for Image Manipulation Detection](https://mlsanity.com/paper/1805.04953/) (2018)
- [Fighting Fake News: Image Splice Detection via Learned Self-Consistency](https://mlsanity.com/paper/1805.04096/) (2018)
- [On Face Segmentation, Face Swapping, and Face Perception](https://mlsanity.com/paper/1704.06729/) (2017)
- [Encryption-then-Compression Systems using Grayscale-based Image Encryption for JPEG Images](https://mlsanity.com/paper/1811.00236/) (2018)
- [Exposing GAN-synthesized Faces Using Landmark Locations](https://mlsanity.com/paper/1904.00167/) (2019)
- [Super-Resolution using Convolutional Neural Networks without Any Checkerboard Artifacts](https://mlsanity.com/paper/1806.02658/) (2018)
- [CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection](https://mlsanity.com/paper/2012.00287/) (2020)
- [Fixed smooth convolutional layer for avoiding checkerboard artifacts in CNNs](https://mlsanity.com/paper/2002.02117/) (2020)
- [Image Manipulation Specifications on Social Networking Services for Encryption-then-Compression Systems](https://mlsanity.com/paper/1810.02062/) (2018)
- [Robust Image Identification for Double-Compressed JPEG Images](https://mlsanity.com/paper/1807.06928/) (2018)

## Built on by (most cited first, 3 of 3)

- [Harnessing Machine Learning for Discerning AI-Generated Synthetic Images](https://mlsanity.com/paper/2401.07358/) (2024)
- [A universal detector of CNN-generated images using properties of checkerboard artifacts in the frequency domain](https://mlsanity.com/paper/2108.01892/) (2021)
- [Determining Intent of Changes to Ascertain Fake Crowdsourced Image Services](https://mlsanity.com/paper/2403.12045/) (2023)

## Abstract (arXiv)

In this paper, we investigate whether robust hashing has a possibility to robustly detect fake-images even when multiple manipulation techniques such as JPEG compression are applied to images for the first time. In an experiment, the proposed fake detection with robust hashing is demonstrated to outperform state-of-the-art one under the use of various datasets including fake images generated with GANs.