Fighting Fake News: Image Splice Detection via Learned Self-Consistency

[paper] [website]

Minyoung Huh *¹², Andrew Liu *¹, Andrew Owens¹, Alexei A. Efros¹
In ECCV 2018.
UC Berkeley, Berkeley AI Research¹
Carnegie Mellon University²

Abstract

In this paper, we introduce a self-supervised method for learning to detect visual manipulations using only unlabeled data. Given a large collection of real photographs with automatically recorded EXIF meta-data, we train a model to determine whether an image is self-consistent -- that is, whether its content could have been produced by a single imaging pipeline.

1) Prerequisites

First clone this repo
git clone --single-branch https://github.com/minyoungg/selfconsistency

All prerequisites should be listed in requirements.txt. The code is written on TensorFlow and is run on Python2.7, we have not verified whether Python3 works. The following command should automatically load any necessary requirements:
bash pip install -r requirements.txt

2) Downloading pretrained model

To download our pretrained-model run the following script in the terminal:
chmod 755 download_model.sh && ./download_model.sh

3) Demo

To run our model on an image run the following code:
python demo.py --im_path=./images/demo.png

We also provide a normalized cut implementation by running the code:
python ncuts_demo.py --im_path=./images/ncuts_demo.png

We have setup a ipython notebook demo here
Disclaimer: Our model works the best on high-resolution natural images. Frames from videos do not generally work well.

Citation

If you find our work useful, please cite:

@inproceedings{huh18forensics,
    title = {Fighting Fake News: Image Splice Detection via Learned Self-Consistency}
    author = {Huh, Minyoung and Liu, Andrew and
              Owens, Andrew and Efros, Alexei A.},
    booktitle = {ECCV},
    year = {2018}
}

Questions

For any further questions please contact Minyoung Huh or Andrew Liu