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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

936
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...
936

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Related Experiment Video

Updated: Jul 16, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Source Camera Identification with a Robust Device Fingerprint: Evolution from Image-Based to Video-Based Approaches.

Manisha1,2, Chang-Tsun Li2, Karunakar A Kotegar1

  • 1Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for identifying the source camera of videos by using a novel device-specific fingerprint. This approach overcomes challenges in video processing, improving multimedia forensics.

Keywords:
PRNUconvolutional neural networkdeep learningmultimedia forensicssource camera identificationvideo forensics

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Area of Science:

  • Digital Forensics
  • Multimedia Security
  • Image and Video Analysis

Background:

  • Digital multimedia content is widespread, increasing the need for accurate source camera identification.
  • Existing image-based methods struggle with video due to processing artifacts like compression and pixel misalignment.
  • High-frequency fingerprints like Photo Response Non-Uniformity (PRNU) are ineffective for video source identification.

Purpose of the Study:

  • To develop a robust method for video source camera identification.
  • To address the limitations of existing techniques in handling video processing disruptions.
  • To unify image and video source identification within a single framework.

Main Methods:

  • Proposed a novel approach leveraging a global stochastic fingerprint in low- and mid-frequency bands.
  • Exploited the resilience of this fingerprint to high-frequency disruptive effects.
  • Utilized a new non-PRNU device-specific fingerprint for unified identification.

Main Results:

  • Established new benchmarks for source camera model and individual device identification on a recent smartphone dataset.
  • Demonstrated superior performance compared to state-of-the-art techniques.
  • Successfully unified image and video source identification.

Conclusions:

  • The novel approach effectively identifies source cameras in videos, overcoming limitations of traditional methods.
  • The non-PRNU fingerprint offers resilience to video processing artifacts.
  • This work advances multimedia forensics by providing a unified framework for image and video source identification.