Real and Fake Face Detection

# Real and Fake Face Detection
**[Computational Intelligence and Photography Lab][1]**
Department of Computer Science, Yonsei University

![Data Samples][2]

## Fake Face Photos by Photoshop Experts

### Introduction
When using social networks, have you ever encountered a 'fake identity'?
Anyone can create a fake profile image using image editing tools, or even using deep learning based generators.
If you are interested in making the world wide web a better place by recognizing such fake faces, you should check this dataset.

### What's Inside and Why
Our dataset contains expert-generated high-quality photoshopped face images.
The images are composite of different faces, separated by eyes, nose, mouth, or whole face.
You may wonder why we need these expensive images other than images automatically generated by computers.
Say we want to train a classifier for real and fake face images.
In case of generative models like [Generative Adversarial Networks][3] (GAN), it is very easy to generate fake face images.
Then, a classifier can be trained using those images, and they do great job discriminating real and generated face images.
We can easily assume that the classifier learns some kind of pattern between images generated by GANs.
However, those patterns can be futile in front of human experts, since exquisite counterfeits by experts are created in completely different process.
Thus we had to create our own dataset with expert level fake face photos.

### Directory and File Information
Inside the parent directory, `training_real`/`training_fake` contains real/fake face photos, respectively.
In case of fake photos, we have three groups; easy, mid, and hard (these groups are separated subjectively, so we do not recommend using them as explicit categories).
Also, you can use the filenames of fake images to see which part of faces are replaced (refer to the image below).
![Filename description.][4]

[1]: https://sites.google.com/site/seonjookim/
[2]: https://github.com/minostauros/Real-and-Fake-Face-Detection/raw/master/samples.jpg
[3]: https://arxiv.org/abs/1406.2661
[4]: https://github.com/minostauros/Real-and-Fake-Face-Detection/raw/master/filename_description.jpg

### Citation
You can cite our dataset as following. [Date Retrieved] should be updated by your own date.
```
Seonghyeon Nam, Seoung Wug Oh, Jae Yeon Kang, Chang Ha Shin, Younghyun Jo, Young Hwi Kim, Kyungmin Kim, Minho Shim, Sungho Lee, Yunji Kim, Suho Han, Gunhee Nam, Dasol Lee, Subin Jeon, In Cho, Woongoh Cho, Sejong Yang, Dongyoung Kim, Hyolim Kang, Sukjun Hwang, and Seon Joo Kim. (2019, January). Real and Fake Face Detection, Version 1. Retrieved [Date Retrieved] from https://www.kaggle.com/datasets/ciplab/real-and-fake-face-detection.
```

## 真伪人脸检测数据集
**[计算智能与摄影实验室][1]**
延世大学计算机科学系

![数据样本][2]

### 精通Photoshop专家制作的虚假人脸照片

#### 简介
在使用社交网络时，您是否曾遭遇过‘虚假身份’的困扰？任何人都可以利用图像编辑工具，甚至基于深度学习的生成器来创建虚假的个人资料图片。如果您有兴趣通过识别此类虚假人脸来使网络世界变得更加美好，那么您应该检查这个数据集。

#### 数据集内容与价值
本数据集包含由专家生成的高质量Photoshop人脸照片。这些照片是由不同的面部特征组合而成，面部特征包括眼睛、鼻子、嘴巴或整个面部。您可能会好奇，为何我们需要的这些昂贵的照片不仅仅是计算机自动生成的图像。假设我们想要训练一个用于识别真实与虚假人脸图像的分类器。在生成对抗网络（GAN）等生成模型的情况下，生成虚假人脸图像是非常容易的。然后，可以使用这些图像来训练分类器，它们在区分真实和生成的人脸图像方面表现出色。我们很容易假设分类器学习到了由GAN生成的图像之间的一些模式。然而，这些模式在面对专家精心制作的、完全不同过程产生的伪造品时，可能是无用的。因此，我们不得不创建我们自己的包含专家级虚假人脸照片的数据集。

#### 目录与文件信息
在父目录中，`training_real`/`training_fake`分别包含真实/虚假的人脸照片。对于虚假照片，我们分为三个难度等级：简单、中等和困难（这些组别是主观划分的，因此我们不推荐将它们作为明确的类别使用）。此外，您还可以通过虚假图像的文件名来查看哪些面部部分被替换（请参阅下方的图像）。

![文件名描述][4]

[1]: https://sites.google.com/site/seonjookim/
[2]: https://github.com/minostauros/Real-and-Fake-Face-Detection/raw/master/samples.jpg
[3]: https://arxiv.org/abs/1406.2661
[4]: https://github.com/minostauros/Real-and-Fake-Face-Detection/raw/master/filename_description.jpg

#### 引用
您可以按照以下格式引用我们的数据集。[日期检索]应由您自行更新。

Seonghyeon Nam, Seoung Wug Oh, Jae Yeon Kang, Chang Ha Shin, Younghyun Jo, Young Hwi Kim, Kyungmin Kim, Minho Shim, Sungho Lee, Yunji Kim, Suho Han, Gunhee Nam, Dasol Lee, Subin Jeon, In Cho, Woongoh Cho, Sejong Yang, Dongyoung Kim, Hyolim Kang, Sukjun Hwang, and Seon Joo Kim. (2019, January). Real and Fake Face Detection, Version 1. Retrieved [Date Retrieved] from https://www.kaggle.com/datasets/ciplab/real-and-fake-face-detection.