Media Forensics Integrity Analytics

Abstract

The goal of this research was to develop a set of forensics tools to determine the integrity, semantic consistency and evolutionary history of images and videos. We used a data-driven approach. In TA1.1 we designed machine-learning methods trained to analyze the integrity of images and videos. In TA1.2 we used the physical integrity of the scene and the traces of electrical network frequency (ENF) to determine the location and integrity of a video. Our work in TA1.3 generated techniques to correlate the existing objects in the pool in space (spatial coherence) and time (time coherence). We have participated in the NIST evaluations and have delivered our software tools via the APIs for integration with the TA2 efforts.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2022
Accession Number
AD1179160

Entities

People

  • C.-c. J. Kuo
  • Edward J. Delp
  • Luisa A. Verdolvia
  • Mauro Barni
  • Nasir Memon
  • Stefano Tubaro
  • Wael AbdAlmageed
  • Walter J. Scheirer

Organizations

  • Purdue University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Engineered Resilient Systems
  • Space

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Automata Theory
  • Bayesian Networks
  • Computational Science
  • Computer Languages
  • Computer Programming
  • Computer Vision
  • Computers
  • Detectors
  • Dimensionality Reduction
  • Feature Extraction
  • Information Processing
  • Information Science
  • Information Systems
  • Network Science
  • Neural Networks
  • Self Organizing Systems
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Image Processing and Computer Vision.
  • Trauma Surgery or Emergency Medicine.

Technology Areas

  • AI & ML
  • Space