More Web Proxy on the site http://driver.im/

research-article

Application of Spectral Information in Identification of Real-Fake Face Images

Authors:

Akhilesh Kumar Pandey,

Rajoo PandeyAuthors Info & Claims

ICCCT '15: Proceedings of the Sixth International Conference on Computer and Communication Technology 2015

Pages 205 - 208

https://doi.org/10.1145/2818567.2818606

Published: 25 September 2015 Publication History

Abstract

The biometric authentication systems based on face recognition are easy to implement with any device, which has an in-built camera. These systems are very secure, but with various attacking methods, such security also becomes vulnerable. Among various types of possible attacks, spoofing is an attack in which available face information is presented before the sensor to mislead the authentication system. In this paper, a model has been presented based on spectral analysis of the captured images to classify them as real faces and face images. We consider Fourier and cosine transform of the image for evaluation of various Image Quality Measures (IQMs), with which the classification is performed using neural networks. The proposed spectral contents based IQMs are compared with several conventional IQMs to judge their performance. The simulation on Replay-Attack database has shown the improvement in the performance of the present model.

References

[1]

I. Avcibas, B. Sankur, and K. Sayood. Statistical evaluation of image quality measures. Electronic Imaging, 11(2):206--223, Apr. 2002.

[2]

S. Bayram, İ. Avcıbaş, B. Sankur, and N. Memon. Image manipulation detection. Journal of Electronic Imaging, 15(4):041102--041102, 2006.

[3]

I. Chingovska, A. Anjos, and S. Marcel. On the Effectiveness of Local Binary Patterns in Face Anti-spoofing. In IEEE BIOSIG 2012, Sept. 2012.

[4]

A. M. Eskicioglu and P. S. Fisher. Image quality measures and their performance. Communications, IEEE Transactions on, 43(12):2959--2965, 1995.

[5]

J. Galbally, S. Marcel, and J. Fierrez. Image quality assessment for fake biometric detection: Application to iris, fingerprint, and face recognition. Image Processing, IEEE Transactions on, 23(2):710--724, 2014.

Digital Library

[6]

Q. Huynh-Thu and M. Ghanbari. Scope of validity of PSNR in image/video quality assessment. Electronics letters, 44(13):800--801, 2008.

[7]

A. K. Jain, K. Nandakumar, and A. Nagar. Biometric template security. EURASIP Journal on Advances in Signal Processing, 2008:113, 2008.

Digital Library

[8]

J. Li, Y. Wang, T. Tan, and A. K. Jain. Live face detection based on the analysis of fourier spectra. In Defense and Security, pages 296--303. International Society for Optics and Photonics, 2004.

[9]

K. A. Nixon, V. Aimale, and R. K. Rowe. Spoof detection schemes. In Handbook of biometrics, pages 403--423. Springer, 2008.

[10]

G. Pan, L. Sun, Z. Wu, and S. Lao. Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, pages 1--8. IEEE, 2007.

[11]

G. Pan, L. Sun, and Z. Wu. Liveness detection for face recognition. INTECH Open Access Publisher, 2008.

[12]

H. A. Rowley, S. Baluja, and T. Kanade. Rotation invariant neural network-based face detection. In Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on, pages 38--44. IEEE, 1998.

Digital Library

[13]

M. C. Stamm and K. R. Liu. Forensic detection of image manipulation using statistical intrinsic fingerprints. Information Forensics and Security, IEEE Transactions on, 5(3):492--506, 2010.

Digital Library

[14]

X. Tan, Y. Li, J. Liu, and L. Jiang. Face liveness detection from a single image with sparse low rank bilinear discriminative model. In Computer Vision--ECCV 2010, pages 504--517. Springer, 2010.

Digital Library

[15]

S. Yao, W. Lin, E. Ong, and Z. Lu. Contrast signal-to-noise ratio for image quality assessment. In Image Processing, 2005. ICIP 2005. IEEE International Conference on, volume 1. IEEE, 2005.

Cited By

Murtaza GMemon RAli SShagari NAli J(2023)Face forgery detection via optimum deep convolution activation feature selection algorithm using expert-generated imagesMultimedia Tools and Applications10.1007/s11042-023-14612-w82:19(28797-28825)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-023-14612-w

Index Terms

Application of Spectral Information in Identification of Real-Fake Face Images
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
  2. Computer graphics
    1. Image manipulation

Recommendations

Merging face and finger images for human identification

Human identification performance reported so far using face or finger images under certain conditions is good practice, however, there is still a great need for better performance in biometrics for use in video surveillance. One possible way to achieve ...
Person identification from heavily occluded face images
SAC '04: Proceedings of the 2004 ACM symposium on Applied computing

In numerous occasions there is need to identify subjects shown in heavily occluded face images. Typical examples include the recognition of criminals whose facial images are captured by surveillance cameras. In such cases a significant part of the ...
Deep architecture-based face spoofing identification in real-time application

Face biometric-based recognition is always a demanding and universally accepted method, especially for access control purposes. However, face recognition systems are usually affected by identity threats, such as face spoofing. Face spoofing is an attempt ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICCCT '15: Proceedings of the Sixth International Conference on Computer and Communication Technology 2015

September 2015

481 pages

ISBN:9781450335522

DOI:10.1145/2818567

Copyright © 2015 ACM.

© 2015 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 September 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICCCT '15

ICCCT '15: Sixth International Conference on Computer and Communication Technology 2015

September 25 - 27, 2015

Allahabad, India

Acceptance Rates

Overall Acceptance Rate 33 of 124 submissions, 27%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
55
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Murtaza GMemon RAli SShagari NAli J(2023)Face forgery detection via optimum deep convolution activation feature selection algorithm using expert-generated imagesMultimedia Tools and Applications10.1007/s11042-023-14612-w82:19(28797-28825)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-023-14612-w

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.

Figures

Tables

Media

View Table of Conten