research-article

Face image manipulation detection based on a convolutional neural network

Authors:

Syed Ibrahim Hassan,

Hyeonjoon MoonAuthors Info & Claims

Volume 129, Issue C

Pages 156 - 168

https://doi.org/10.1016/j.eswa.2019.04.005

Published: 01 September 2019 Publication History

Highlights

•

Proposing MANFA – a customized CNN model for manipulated face detection.

•

Integrating XGBoost, and AdaBoost with MANFA to cope with the extreme imbalanced dataset.

•

Proposing a manually collected dataset (8950 images) for altered face detection.

Abstract

Facial image manipulation is a particular instance of digital image tampering, which is done by compositing a region from one facial image into another facial image. Fake images generated by facial image manipulation now spread like wildfire on news websites and social networks, and are considered the greatest threat to press freedom. Previous research relied heavily on handcrafted features to analyze tampered regions which were inefficient and time-consuming. This paper introduces a framework that accurately detects manipulated face image using deep learning approach. The original contributions of this paper include (1) a customized convolutional neural network model for Manipulated Face (MANFA) identification; it contains several convolutional layers that effectively extract features of multi-levels of abstraction from a tampered region. (2) A hybrid framework (HF-MANFA) that uses Adaptive Boosting (AdaBoost) and eXtreme Gradient Boosting (XGBoost) to deal with the imbalanced dataset challenge. (3) A large manipulated face dataset that is manually collected and validated. The results from various experiments proved that proposed models outperformed existing expert and intelligent systems which were usually used for the manipulated face image detection task in terms of area under the curve (AUC), computational complexity, and robustness against imbalanced datasets. As a result, the presented framework will motivate the finding of a more powerful altered face images detection method and encourages the integration of the proposed model in applications that have to deal with manipulated images regularly.

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Cited By

Umair MBouguettaya ALakhdari AOuzzani MLiu Y(2024)Exif2Vec: A Framework to Ascertain Untrustworthy Crowdsourced Images Using MetadataACM Transactions on the Web10.1145/364509418:3(1-27)Online publication date: 13-Feb-2024
https://dl.acm.org/doi/10.1145/3645094
Guo ZYang GChen JSun X(2023)Exposing Deepfake Face Forgeries With Guided ResidualsIEEE Transactions on Multimedia10.1109/TMM.2023.323716925(8458-8470)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2023.3237169
Yu YNi RLi WZhao Y(2022)Detection of AI-Manipulated Fake Faces via Mining Generalized FeaturesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/349902618:4(1-23)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3499026
Show More Cited By

Index Terms

Face image manipulation detection based on a convolutional neural network
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Information

Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 129, Issue C

Sep 2019

311 pages

ISSN:0957-4174

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 September 2019

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Cited By

Umair MBouguettaya ALakhdari AOuzzani MLiu Y(2024)Exif2Vec: A Framework to Ascertain Untrustworthy Crowdsourced Images Using MetadataACM Transactions on the Web10.1145/364509418:3(1-27)Online publication date: 13-Feb-2024
https://dl.acm.org/doi/10.1145/3645094
Guo ZYang GChen JSun X(2023)Exposing Deepfake Face Forgeries With Guided ResidualsIEEE Transactions on Multimedia10.1109/TMM.2023.323716925(8458-8470)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2023.3237169
Yu YNi RLi WZhao Y(2022)Detection of AI-Manipulated Fake Faces via Mining Generalized FeaturesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/349902618:4(1-23)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3499026
Juefei-Xu FWang RHuang YGuo QMa LLiu Y(2022)Countering Malicious DeepFakes: Survey, Battleground, and HorizonInternational Journal of Computer Vision10.1007/s11263-022-01606-8130:7(1678-1734)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s11263-022-01606-8
Wang YHuang LYee A(2022)Full-convolution Siamese network algorithm under deep learning used in tracking of facial video image in newbornsThe Journal of Supercomputing10.1007/s11227-022-04439-x78:12(14343-14361)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s11227-022-04439-x

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