short-paper

Detecting Both Machine and Human Created Fake Face Images In the Wild

Authors:

Simon S. WooAuthors Info & Claims

MPS '18: Proceedings of the 2nd International Workshop on Multimedia Privacy and Security

Pages 81 - 87

https://doi.org/10.1145/3267357.3267367

Published: 15 January 2018 Publication History

Abstract

Due to the significant advancements in image processing and machine learning algorithms, it is much easier to create, edit, and produce high quality images. However, attackers can maliciously use these tools to create legitimate looking but fake images to harm others, bypass image detection algorithms, or fool image recognition classifiers. In this work, we propose neural network based classifiers to detect fake human faces created by both 1) machines and 2) humans. We use ensemble methods to detect GANs-created fake images and employ pre-processing techniques to improve fake face image detection created by humans. Our approaches focus on image contents for classification and do not use meta-data of images. Our preliminary results show that we can effectively detect both GANs-created images, and human-created fake images with 94% and 74.9% AUROC score.

References

[1]

Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. ACM, 785--794.

Digital Library

[2]

Fran¸cois Chollet. 2017. Xception: Deep learning with depthwise separable convolutions. arXiv preprint arXiv:1610.02357 (2017).

[3]

Fran¸cois Chollet et al. 2015. Keras. https://keras.io.

[4]

Jon Christian. 2018. Experts fear face swapping tech could start an international showdown. Retrieved July 5, 2018 from https://theoutline.com/post/3179/ deepfake-videos-are-freaking-experts-out

[5]

Hyoungseok Chu. 2017. AUROC. Retrieved Jun 25, 2018 from https://github.com/hyoungseokchu/AUROC

[6]

Samantha Cole. 2018. We Are Truly Fucked: Everyone Is Making AI-Generated Fake Porn Now. Vice (25 Jan 2018). https://motherboard.vice.com/en us/article/bjye8a/ reddit-fake-porn-app-daisy-ridley

[7]

Davide Cozzolino, Giovanni Poggi, and Luisa Verdoliva. 2017. Recasting residual-based local descriptors as convolutional neural networks: an application to image forgery detection. In Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security. ACM, 159--164.

Digital Library

[8]

Iv´an de Paz Centeno. 2018. MTCNN. Retrieved Jun 25, 2018 from https://github.com/ipazc/mtcnn

[9]

Hany Farid. 2009. Exposing digital forgeries from JPEG ghosts. IEEE Trans. Information Forensics and Security 4, 1 (2009), 154--160.

Digital Library

[10]

Mohammad Farukh Hashmi, Aaditya R Hambarde, and Avinash G Keskar. 2013. Copy move forgery detection using DWT and SIFT features. In Intelligent Systems Design and Applications (ISDA), 2013 13th International Conference on. IEEE, 188--193.

[11]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.

[12]

Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. 2017. Densely Connected Convolutional Networks. In CVPR '17, Vol. 1. 3.

[13]

Multimedia Computing Lab in Kaist. 2015. MMC Image Forensic Tool. Retrieved July 22, 2018 from http://forensic.kaist.ac.kr/

[14]

Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2017. Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017).

[15]

A. Kashyap, B. Suresh, M. Agrawal, H. Gupta, and S. D. Joshi. 2015. Detection of splicing forgery using wavelet decomposition. In International Conference on Computing, Communication Automation. 843--848.

[16]

Davis E King. 2009. Dlib-ml: A machine learning toolkit. Journal of Machine Learning Research 10 (July 2009), 1755--1758.

Digital Library

[17]

Neal Krawetz. 2007. A Picture's Worth: Digital Image Analysis and Forensics. Hacker Factor Solutions (2007), 16--20.

[18]

Neal Krawetz. 2012. FotoForensics. Retrieved July 5, 2018 from http://fotoforensics.com/

[19]

Zhouchen Lin, Junfeng He, Xiaoou Tang, and Chi-Keung Tang. 2009. Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis. Pattern Recognition 42, 11 (2009), 2492--2501.

Digital Library

[20]

S. Murali, Govindraj B. Chittapur, H. S. Prabhakara, and Basavaraj S. Anami. 2013. Comparision and analysis of photo image forgery detection techniques. arXiv preprint arXiv:1302.3119 (2013).

[21]

Joseph Redmon and Ali Farhadi. 2018. YOLOv3: An Incremental Improvement. arXiv preprint arXiv:1804.02767 (2018).

[22]

Aja Romano. 2018. Jordan Peele's simulated Obama PSA is a double-edged warning against fake news. Retrieved August 15, 2018 from https://www.vox.com/2018/4/18/17252410/ jordan-peele-obama-deepfake-buzzfeed

[23]

Kevin Roose. 2018. Here Come the Fake Videos, Too. Retrieved July 5, 2018 from https://www.nytimes.com/2018/03/04/ technology/fake-videos-deepfakes.html

[24]

Andreas R¨ossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. 2018. FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces. arXiv preprint arXiv:1803.09179 (2018).

[25]

seetaface. 2016. SeetaFaceEngine. Retrieved Jun 25, 2018 from https://github.com/seetaface/SeetaFaceEngine

[26]

Karen Simonyan and Andrew Zisserman. 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:1409.1556 (2014), 1--14.

[27]

Justus Thies, Michael Zollhofer, Marc Stamminger, Christian Theobalt, and Matthias Nießner. 2016. Face2face: Real-time face capture and reenactment of rgb videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2387--2395.

Digital Library

[28]

Wikipedia. 2018. Deepfake. Retrieved July 5, 2018 from https: //en.wikipedia.org/wiki/Deepfake

[29]

Jianquan Yang, Guopu Zhu, Jiwu Huang, and Xi Zhao. 2015. Estimating JPEG compression history of bitmaps based on factor histogram. Digital Signal Processing 41 (2015), 90--97.

Digital Library

[30]

Saber Zerdoumi, Aznul Qalid Md Sabri, Amirrudin Kamsin, Ibrahim Abaker Targio Hashem, Abdullah Gani, Saqib Hakak, Mohammed Ali Al-Garadi, and Victor Chang. 2017. Image pattern recognition in big data: taxonomy and open challenges: survey. Multimedia Tools and Applications (2017), 1--31.

Digital Library

[31]

Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, and Yu Qiao. 2016. Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks. arXiv prerpint arXiv:1604.02878 (2016).

[32]

Peng Zhou, Xintong Han, Vlad I. Morariu, and Larry S. Davis. 2018. Learning Rich Features for Image Manipulation Detection. arXiv preprint arXiv:1805.04953 (2018).

[33]

Xiaogang Wang Ziwei Liu, Ping Luo and Xiaoou Tang. 2015. Deep Learning Face Attributes in the Wild. In Proceedings of International Conference on Computer Vision (ICCV '15).

Digital Library

[34]

Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le. 2017. Learning Transferable Architectures for Scalable Image Recognition. arXiv preprint arXiv:1707.07012 (2017).

Cited By

Yanamala Bhuvaneswari Sanjana Samba Nemani Hiranmayai Dr Halavath Balaji (2024)Robust Deepfake Detection System with Deep Learning TechniquesInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-17608(41-49)Online publication date: 24-Apr-2024
https://doi.org/10.48175/IJARSCT-17608
Zhou QZhou ZBao ZNiu WLiu Y(2024)IIN-FFD: Intra-Inter Network for Face Forgery DetectionTsinghua Science and Technology10.26599/TST.2024.901002229:6(1839-1850)Online publication date: Dec-2024
https://doi.org/10.26599/TST.2024.9010022
Wang TLiao XChow KLin XWang Y(2024)Deepfake Detection: A Comprehensive Survey from the Reliability PerspectiveACM Computing Surveys10.1145/369971057:3(1-35)Online publication date: 11-Nov-2024
https://dl.acm.org/doi/10.1145/3699710
Show More Cited By

Index Terms

Detecting Both Machine and Human Created Fake Face Images In the Wild
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Social aspects of security and privacy

Recommendations

Detecting fake images using watermarks and support vector machines

With the great convenience of computer graphics and digital imaging, it becomes much easier to alter the content of images than before without any visually traces to catch these manipulations, i.e., many fake images are produced whose content is ...
DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models
CCS '23: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security

Text-to-image generation models that generate images based on prompt descriptions have attracted an increasing amount of attention during the past few months. Despite their encouraging performance, these models raise concerns about the misuse of their ...
Detection of Fake Remote-Sensing Data
Abstract
The paper deals with the detection of counterfeit remote-sensing data. A remote sensing data detector is considered that uses the neural-net identification of spectral artifacts typical of adversarial generators. The detector is employed to spot ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

MPS '18: Proceedings of the 2nd International Workshop on Multimedia Privacy and Security

October 2018

110 pages

ISBN:9781450359887

DOI:10.1145/3267357

General Chairs:
Roger A. Hallman
SPAWAR Systems Center Pacific; Dartmouth College, USA
,
Shujun Li
University of Kent, UK
,
Victor Chang
Xi'an Jiaotong-Liverpool University, China

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGSAC: ACM Special Interest Group on Security, Audit, and Control

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 January 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

MSIT(Ministry of Science and ICT) Korea
NRF of Korea

Conference

CCS '18

Sponsor:

SIGSAC

CCS '18: 2018 ACM SIGSAC Conference on Computer and Communications Security

October 15 - 19, 2018

Toronto, Canada

Acceptance Rates

MPS '18 Paper Acceptance Rate 2 of 4 submissions, 50%;

Overall Acceptance Rate 5 of 11 submissions, 45%

Upcoming Conference

CCS '25

Sponsor:
sigsac

ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

116
Total Citations
View Citations
2,398
Total Downloads

Downloads (Last 12 months)219
Downloads (Last 6 weeks)19

Reflects downloads up to 18 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yanamala Bhuvaneswari Sanjana Samba Nemani Hiranmayai Dr Halavath Balaji (2024)Robust Deepfake Detection System with Deep Learning TechniquesInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-17608(41-49)Online publication date: 24-Apr-2024
https://doi.org/10.48175/IJARSCT-17608
Zhou QZhou ZBao ZNiu WLiu Y(2024)IIN-FFD: Intra-Inter Network for Face Forgery DetectionTsinghua Science and Technology10.26599/TST.2024.901002229:6(1839-1850)Online publication date: Dec-2024
https://doi.org/10.26599/TST.2024.9010022
Wang TLiao XChow KLin XWang Y(2024)Deepfake Detection: A Comprehensive Survey from the Reliability PerspectiveACM Computing Surveys10.1145/369971057:3(1-35)Online publication date: 11-Nov-2024
https://dl.acm.org/doi/10.1145/3699710
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
Alam IMuneer MWoo SSerra ESpezzano F(2024)UGAD: Universal Generative AI Detector utilizing Frequency FingerprintsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680085(4332-4340)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680085
Seraj MSingh AChakraborty S(2024)Semi-Supervised Deep Domain Adaptation for Deepfake Detection2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)10.1109/WACVW60836.2024.00116(1061-1071)Online publication date: 1-Jan-2024
https://doi.org/10.1109/WACVW60836.2024.00116
Demir İÇiftçi U(2024)How Do Deepfakes Move? Motion Magnification for Deepfake Source Detection2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00471(4768-4778)Online publication date: 3-Jan-2024
https://doi.org/10.1109/WACV57701.2024.00471
Wang YLiao G(2024)Deepfake Video Detection Based on Image Source Anomaly2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA)10.1109/ICIPCA61593.2024.10709022(397-401)Online publication date: 28-Jun-2024
https://doi.org/10.1109/ICIPCA61593.2024.10709022
Tariq RTariq SWoo S(2024)Exploring the Impact of Moire Pattern on Deepfake Detectors2024 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP51287.2024.10647902(3813-3819)Online publication date: 27-Oct-2024
https://doi.org/10.1109/ICIP51287.2024.10647902
Tariq RHeo MWoo STariq S(2024)Beyond the Screen: Evaluating Deepfake Detectors under Moiré Pattern Effects2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00446(4429-4439)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00446
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents