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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Related Experiment Video

Updated: Aug 28, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

850

Frame-rate up-conversion detection based on convolutional neural network for learning spatiotemporal features.

Minseok Yoon1, Seung-Hun Nam2, In-Jae Yu3

  • 1School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.

Forensic Science International
|September 18, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a Frame-Rate Conversion Detection Network (FCDNet) to identify manipulated videos. The FCDNet effectively detects subtle artifacts from frame interpolation, enhancing video forensics capabilities.

Keywords:
Convolutional neural networkFrame interpolation schemeFrame-rate conversion detectionResidual featuresSpatiotemporal featuresVideo forensics

Related Experiment Videos

Last Updated: Aug 28, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

850

Area of Science:

  • Digital Forensics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Advanced video editing tools enable sophisticated video manipulation.
  • Frame-rate up-conversion (FRUC) is a technique used in video tampering, leaving subtle, hard-to-detect artifacts.
  • Detecting FRUC forgery is crucial for video integrity and authenticity.

Purpose of the Study:

  • To propose a novel deep learning network, FCDNet, for detecting frame-rate up-conversion (FRUC) artifacts.
  • To develop a method that learns forensic features of FRUC in an end-to-end manner.
  • To create a robust and efficient system for identifying manipulated videos.

Main Methods:

  • Developed a Frame-Rate Conversion Detection Network (FCDNet) utilizing a stack of consecutive frames.
  • Employed network blocks to learn spatiotemporal features and interpolation artifacts.
  • The network supports detection across nearest neighbor, bilinear, and motion-compensated interpolation schemes.

Main Results:

  • FCDNet achieved outstanding performance in detecting FRUC-induced interpolated artifacts.
  • The model demonstrated robustness against unseen datasets, frame rates, and quality factors.
  • Achieved high detection speed by analyzing only six frames, outperforming methods using all frames.

Conclusions:

  • FCDNet offers a highly effective and efficient solution for detecting frame-rate up-conversion in videos.
  • The proposed method provides robust detection capabilities and can precisely localize tampered regions temporally.
  • This work significantly advances the field of digital video forensics by addressing sophisticated manipulation techniques.