Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

918
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
918
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

2.1K
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
2.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Original status of smartphone video by analyzing the data volume according to the recording time.

Journal of forensic sciences·2021
Same author

A method of smart phone original video identification by using unique compression ratio pattern.

Forensic science international·2019
Same author

Identification of Mobile Phone and Analysis of Original Version of Videos through a Delay Time Analysis of Sound Signals from Mobile Phone Videos.

Journal of forensic sciences·2017
Same journal

Commentary on: Maskell PD, de Korompay A. Letter to the Editor-The transition point from zero-order to first order in blood alcohol elimination curves. Where is it? J Forensic Sci. 2025;70 (1):398-400. https://doi.org/10.1111/1556-4029.15650.

Journal of forensic sciences·2026
Same journal

A novel relationship between time offsets in capillary electrophoresis and DNA sequence variations in short tandem repeats.

Journal of forensic sciences·2026
Same journal

A 4-zone model to determine fentanyl overdose probability.

Journal of forensic sciences·2026
Same journal

Authors' response.

Journal of forensic sciences·2026
Same journal

Determining the utility of radio frequency identification (RFID) technology for disaster victim identification (DVI).

Journal of forensic sciences·2026
Same journal

Stakeholders' perspectives on integrating point-of-care diagnostics into forensic death investigations in South Africa.

Journal of forensic sciences·2026
See all related articles

Related Experiment Video

Updated: Apr 28, 2026

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

8.7K

Identification method for digital image forgery and filtering region through interpolation.

Min Gu Hwang1, Dong Hwan Har

  • 1Digital Scientific Imaging Laboratory, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, 221 Huksuk-dong, Dongjak-ku, Seoul, Korea.

Journal of Forensic Sciences
|June 5, 2014
PubMed
Summary
This summary is machine-generated.

Digital image forensics can detect forgeries using interpolation analysis. This study proposes an improved algorithm to accurately identify digitally manipulated composite image regions by analyzing pixel patterns.

Keywords:
composite imagedigital image forgerydigital tamperingforensic scienceimage filteringinterpolationphotograph fakery

More Related Videos

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

11.5K
Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens
09:29

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Published on: January 24, 2016

9.2K

Related Experiment Videos

Last Updated: Apr 28, 2026

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

8.7K
Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

11.5K
Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens
09:29

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Published on: January 24, 2016

9.2K

Area of Science:

  • Digital Image Forensics
  • Computer Vision
  • Image Processing

Background:

  • Digital composite images are increasingly prevalent, leading to a rise in digital forgery.
  • Interpolation is a common image editing technique used to naturalize composite images, leaving detectable traces.
  • Existing algorithms for detecting interpolation artifacts in composite regions have limitations.

Purpose of the Study:

  • To analyze pixel patterns in interpolated and non-interpolated image regions.
  • To propose a novel detection map algorithm for distinguishing these regions.
  • To develop an improved algorithm for identifying composite regions based on interpolation traces.

Main Methods:

  • Analysis of pixel patterns in digital images.
  • Development of a detection map algorithm.
  • Application of minimum filter, Laplacian operation, and maximum filters for improved detection.

Main Results:

  • The proposed algorithm effectively separates interpolated and non-interpolated regions.
  • Improved detection of composite regions by analyzing interpolation artifacts.
  • Successful analysis of filtering regions that utilized interpolation operations.

Conclusions:

  • The developed algorithm enhances the accuracy of digital image forgery detection.
  • This method provides a more robust approach to identifying composite regions in digital images.
  • The findings contribute to advancements in digital image forensics and authenticity verification.