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WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection

Abstact

In recent years, the abuse of a face swap technique called deepfake Deepfake has raised enormous public concerns. Deepfake manipulates deep learning techniques to replace one person's face in a video with someone else's without leaving obvious traces. So far, a large number of deepfake videos (also known as "deepfakes") have been crafted and uploaded to the internet, which calls for the development of effective countermeasures. One promising countermeasure against deepfakes is deepfake detection. Several deepfake datasets have been released to support the training and testing of deepfake detectors, such as DeepfakeDetection and FaceForensics++. While this has greatly advanced deepfake detection, most of the real videos in these datasets are filmed with a few volunteer actors in limited scenes. The fake videos are crafted by researchers using a few popular deepfake software. Detectors developed on these datasets may lose effectiveness when applied to detect the vast variety of deepfake videos in the wild (those uploaded to various video-sharing websites). To better support detection against real-world deepfakes, in this paper, we introduce a new dataset WildDeepfake, which consists of 7,314 face sequences extracted from 707 deepfake videos that are collected completely from the internet. WildDeepfake is a small dataset that can be used, in addition to existing datasets, to develop more effective detectors against real-world deepfakes. We systematically evaluate a set of baseline detection networks on both existing and our WildDeepfake datasets, and show that WildDeepfake is indeed a more challenging dataset, where the detection performance can decrease drastically. We also propose two (eg. 2D and 3D) Attention-based Deepfake Detection Networks (ADDNets) to leverage the attention masks on real/fake faces for improved detection. We empirically verify the effectiveness of ADDNets on both existing and WildDeepfake.

<p align="center"> <img src="./fakemask.jpg" width="480px" height="290px" alt="Deepfake in the Wild" title="Deepfake in the Wild" align="center"></img> </p>

The new manager of this dataset is Prof. Xingjun Ma. If you have questions, feel free to send email to him (danxjma@gmail.com)

FACE PRIVACY Statement

We have done the following to ensure face privacy in the dataset.

Introduction

Previous datasets

Dataset nameDownloadGenerate methodDeepfake videosActors
Deepfake-TIMIT lowdownloadDeepfake32032
Deepfake-TIMIT highdownloadDeepfake32032
Faceforensics-Deepfake1000977
Faceforensics++downloadDeepfake1000977
Deepfake detectiondownloadDeepfakeover300028
Celeb-deepfakeforensics v1downloadDeepfake79513
Celeb-deepfakeforensics v2downloadDeepfake59059
DFDCdownloadDeepfake--

Ours

Dataset nameDownloadGenerate methodDeepfake videosActors
WildDeepfakedownloadInternet707-

File Structure:

deepfake_in_the_wild
                    |--real train
                                 |--0.tar.gz
                                 |--1.tar.gz
                                 |--2.tar.gz
                                 ...
                    |--real test
                                |--0.tar.gz
                                |--1.tar.gz
                                |--2.tar.gz
                                ...
                    |--fake train
                                 |--0.tar.gz
                                 |--1.tar.gz
                                 |--2.tar.gz
                                 ...
                    |--fake test
                                |--0.tar.gz
                                |--1.tar.gz
                                |--2.tar.gz
                                ...

In each tar.gz file, there will be several folders containing face images, and the images in each folder represent a face sequence. The image name in the folder represents the frame number it appears in the original video.

Our Method

<p align="center"> <img src="./ADDNet.png" alt="ADDNet" title="ADDNet" align="center"></img> </p> The network structure of our proposed ADDNet-2D is illustrated below. Detailed structures of the three residual blocks used in our ADDNet-2D network are shown below too. These three blocks are also the building blocks of XceptionNet. The base network before the "resblock3" is our proposed ADDblock. Our ADDNet-3D shares the same ADD block architecture as ADDNet-2D, but has one ADD block for each of the face images in the sequence. Therefore, in our setting with face sequence length 𝐿, ADDNet-3D will have 𝐿 ADDblocks, and each ADD blocks share the same weights. Also different from ADDNet-2D, the classifier network(structure after the ADDblock) of ADDNet-3D is a 3D CNN. <p align="center"> <img src="./details.jpg" alt="details" title="detials" align="center"></img> </p>

Expriments

First, we use pre-trained Resnet-101 to extract features from the images in previous datasets and our dataset. Then we use the T-SNE to reduce the dimensionality. Red points represent fake faces, green points represent real faces. Here is the comparison:

<p align="center"> <img src="./t-sne.PNG" alt="t-sne" title="t-sne" align="center"></img> </p>

Download

You can fill the form here to request a copy of the dataset. We support both Baidu Drive and Google Drive.