Refining PRNU-Based Detection of Image Forgeries

Xufeng Lin, Chang-Tsun Li

Research output: Book chapter/Published conference paperConference paper

2 Citations (Scopus)

Abstract

Photo Response Non-Uniformity (PRNU) noise can be considered as a spread-spectrum watermark embedded in every image taken by the source imaging device. It has been effectively used for localizing the forgeries in digital images. The noise residual extracted from the image in question is compared with the reference PRNU in a sliding-window based manner. If their normalized cross correlation, which servers as a decision statistic, is below a pre-determined threshold (e.g., by Neyman Pearson criterion), the center pixel in the window is declared as forged. However, the decision statistic is calculated over the forged and the non-forged regions when the sliding window falls near the boundary 01' the two different regions. As a result, the corresponding pixels of the forged region are probably wrongly identified as genuine ones. To alleviate this problem, we analyze the correlation distribution in the problematic region and refine the detection by weighting the decision threshold based on the altered correlation distribution. The effectiveness of the proposed refining algorithm is confirmed through the results of detecting three different kinds 01' realistic image forgeries.
Original languageEnglish
Title of host publication Digital Media Industry & Academic Forum (DMIAF)
Place of PublicationUnited States
PublisherIEEE Xplore
Pages222-226
Number of pages5
ISBN (Electronic)9781509010004
ISBN (Print)9781509010011
DOIs
Publication statusPublished - 2016
Event2016 Digital Media Industry & Academic Forum (DMIAF) - Santorini, Greece, Santorini, Greece
Duration: 04 Jul 201606 Jul 2016

Conference

Conference2016 Digital Media Industry & Academic Forum (DMIAF)
Abbreviated titleDigital media for the connected world
CountryGreece
CitySantorini
Period04/07/1606/07/16

Cite this

Lin, X., & Li, C-T. (2016). Refining PRNU-Based Detection of Image Forgeries. In Digital Media Industry & Academic Forum (DMIAF) (pp. 222-226). [WB1.4 ] IEEE Xplore. https://doi.org/10.1109/DMIAF.2016.7574937