research-article

On the influence of denoising in PRNU based forgery detection

Authors:

Luisa VerdolivaAuthors Info & Claims

MiFor '10: Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence

Pages 117 - 122

https://doi.org/10.1145/1877972.1878002

Published: 29 October 2010 Publication History

Get Access

Abstract

To detect some image forgeries one can rely on the Photo-Response Non-Uniformity (PRNU), a deterministic pattern associated with each individual camera, which can be loosely modeled as low-intensity multiplicative noise. A very promising algorithm for PRNU-based forgery detection has been recently proposed by Chen et al. Image denoising is a key step of the algorithm, since it allows to single out and remove most of the signal components and reveal the PRNU pattern. In this work we analyze the influence of denoising on the overall performance of the method and show that the use of a suitable state-of-the art denoising technique improves performance appreciably w.r.t. the original algorithm.

References

[1]

I.Amerini, R.Caldelli, V.Cappellini, F.Picchioni, and A.Piva. Analysis of denoising filters for photo response non uniformity noise extraction in source camera identification. In Proceedings of Digital Signal Processing, pages 1--7, Santorini, Greece, Jul. 2009.

Digital Library

Google Scholar

[2]

A.Buades, B.Coll, and J.M.Morel. A review of image denoising algorithms,with a new one. Multiscale Model. Simul., 4(2):490--530. July 2005.

Crossref

Google Scholar

[3]

M.Chen, J.Fridrich, M.Goljan, and J.Luká. Determining image origin and integrity using sensor noise. IEEE Transactions on Information Forensics and Security, 3(1):74--90, Mar. 2008.

Digital Library

Google Scholar

[4]

K.Dabov, A.Foi, V.Katkovnik, and K.Egiazarian. Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Transactions on Image Processing, 16(8):2080--2095, Aug. 2007.

Digital Library

Google Scholar

[5]

J.Fridrich. Digital image forensics. IEEE Signal Processing Magazine, 26(2):26--37, Mar. 2009.

Crossref

Google Scholar

[6]

J.Fridrich, D.Soukal, and J.Lukás. Detection of copy move forger in digital images. In Proc. Digital Forensic Research Workshop. 2003.

Google Scholar

[7]

R.G.E.Healey. Radiometric ccd camera calibration and noise estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(3):267--275, Mar. 1994.

Digital Library

Google Scholar

[8]

J.He, Z.Lin, L.Wang, and X.Tang. Detecting doctored jpeg images via dct coefficient analysis. In Proc. of 9th European Conference on Computer Vision, pages 423--435, Graz, Austria, May 2006.

Digital Library

Google Scholar

[9]

J.Lukas, J.Fridrich, and M.Goljan. Detecting digital image forgeries using sensor pattern noise. In SPIE Electronic Imaging Forensics, Security, Steganography, and Watermarking of Multimedia Contents VIII, volume 6072, pages 362--372, San Jose, CA, USA, Jan. 2006.

Google Scholar

[10]

M.K.Mihcak, I.Kozintsev, and K.Ramchandran. Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pages 3253--3256, Phoenix, AZ, USA, Mar. 1999.

Digital Library

Google Scholar

[11]

A.C.Popescu and H.Farid. Exposing digital forgeries by detecting duplicated image regions. Dept. Comput. sci., Dartmouth College. Tech. Rep. TR2004--515, 2004.

Google Scholar

[12]

A.C.Popescu and H.Farid. Exposing digital forgeries by detecting traces of re-sampling. IEEE Transactions on Signal Processing. 53(2):758--767, Feb. 2005.

Digital Library

Google Scholar

[13]

S.Ye, Q.Sun, and E.C. Chang. Detecting digital image forgeries by measuring inconsistencies of blocking artifact. In Proc. IEEE Int. Conf. Multimedia and Expo, pages 12--15, Beijing, China, 2007.

Crossref

Google Scholar

Cited By

View all

Liu YXiao YTian H(2024)Plug-and-Play PRNU Enhancement Algorithm with Guided FilteringSensors10.3390/s2423770124:23(7701)Online publication date: 2-Dec-2024
https://doi.org/10.3390/s24237701
Fernández-Menduiña SPérez-González FMasciopinto M(2023)Source camera attribution via PRNU emphasis: Towards a generalized multiplicative modelSignal Processing: Image Communication10.1016/j.image.2023.116944114(116944)Online publication date: May-2023
https://doi.org/10.1016/j.image.2023.116944
Mazaheri GRoy-Chowdhury A(2022)Detection and Localization of Facial Expression Manipulations2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV51458.2022.00283(2773-2783)Online publication date: Jan-2022
https://doi.org/10.1109/WACV51458.2022.00283
Show More Cited By

Index Terms

On the influence of denoising in PRNU based forgery detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation
      1. Image processing

Recommendations

PRNU-based Deepfake Detection
IH&MMSec '21: Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia Security

As deepfakes become harder to detect by humans, more reliable detection methods are required to fight the spread of fake images and videos. In our work, we focus on PRNU-based detection methods, which, while popular in the image forensics scene, have ...
Forgery detection using feature-clustering in recompressed JPEG images

JPEG images are widely used in a large range of applications. The properties of JPEG compression can be used for detection of forgery in digital images. The forgery in JPEG images requires the image to be resaved thereby, re-compression of image. ...
Detection of copy-rotate-move forgery using Zernike moments
IH'10: Proceedings of the 12th international conference on Information hiding

As forgeries have become popular, the importance of forgery detection is much increased. Copy-move forgery, one of the most commonly used methods, copies a part of the image and pastes it into another part of the the image. In this paper, we propose a ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

MiFor '10: Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence

October 2010

134 pages

ISBN:9781450301572

DOI:10.1145/1877972

Program Chairs:
Sebastiano Battiato
University of Catania, Italy
,
Sabu Emmanuel
Nanyang Technological University, Singapore
,
Adrian Ulges
German Research Center for Artificial Intelligence, Germany
,
Marcel Worring
University of Amsterdam, The Netherlands

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MM '10

Sponsor:

SIGMM

MM '10: ACM Multimedia Conference

October 29, 2010

Firenze, Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

58
Total Citations
View Citations
568
Total Downloads

Downloads (Last 12 months)15
Downloads (Last 6 weeks)7

Reflects downloads up to 12 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Liu YXiao YTian H(2024)Plug-and-Play PRNU Enhancement Algorithm with Guided FilteringSensors10.3390/s2423770124:23(7701)Online publication date: 2-Dec-2024
https://doi.org/10.3390/s24237701
Fernández-Menduiña SPérez-González FMasciopinto M(2023)Source camera attribution via PRNU emphasis: Towards a generalized multiplicative modelSignal Processing: Image Communication10.1016/j.image.2023.116944114(116944)Online publication date: May-2023
https://doi.org/10.1016/j.image.2023.116944
Mazaheri GRoy-Chowdhury A(2022)Detection and Localization of Facial Expression Manipulations2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV51458.2022.00283(2773-2783)Online publication date: Jan-2022
https://doi.org/10.1109/WACV51458.2022.00283
Cozzolino DVerdoliva L(2022)Multimedia Forensics Before the Deep Learning EraHandbook of Digital Face Manipulation and Detection10.1007/978-3-030-87664-7_3(45-67)Online publication date: 31-Jan-2022
https://doi.org/10.1007/978-3-030-87664-7_3
Rouhi RBertini FMontesi D(2021)No Matter What Images You Share, You Can Probably Be Fingerprinted AnywayJournal of Imaging10.3390/jimaging70200337:2(33)Online publication date: 11-Feb-2021
https://doi.org/10.3390/jimaging7020033
de Roos LGeradts Z(2021)Factors that Influence PRNU-Based Camera-Identification via VideosJournal of Imaging10.3390/jimaging70100087:1(8)Online publication date: 13-Jan-2021
https://doi.org/10.3390/jimaging7010008
Wang BWang YHou JSui XZhu M(2021)Discriminative feature projection for camera model identification of recompressed imagesMultimedia Tools and Applications10.1007/s11042-021-11201-7Online publication date: 10-Jul-2021
https://doi.org/10.1007/s11042-021-11201-7
Bruni VTartaglione MVitulano D(2021)Coherence of PRNU weighted estimations for improved source camera identificationMultimedia Tools and Applications10.1007/s11042-020-10477-581:16(22653-22676)Online publication date: 2-Feb-2021
https://doi.org/10.1007/s11042-020-10477-5
Yuting SJing GLing DYongzhen K(2020)Blind Detection of Partial-Color-Manipulation Based on Self-PRNU EstimationDigital Forensics and Forensic Investigations10.4018/978-1-7998-3025-2.ch009(103-116)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-3025-2.ch009
Pu JMangaokar NWang BReddy CViswanath B(2020)NoiseScope: Detecting Deepfake Images in a Blind SettingProceedings of the 36th Annual Computer Security Applications Conference10.1145/3427228.3427285(913-927)Online publication date: 7-Dec-2020
https://dl.acm.org/doi/10.1145/3427228.3427285
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.

Cited By

Index Terms

Recommendations

PRNU-based Deepfake Detection

Forgery detection using feature-clustering in recompressed JPEG images

Detection of copy-rotate-move forgery using Zernike moments