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Deep Learning And Multi-statistical Features: An Intra-frame Forgery Detection Video Method.

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Deep Learning And Multi-statistical Features: An Intra-frame Forgery Detection Video Method.

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Related Experiment Videos

Deep learning and multi-statistical features: an intra-frame forgery detection video method.

Diaa Uliyan¹, Manal Eid Alazmi², Mohammad Alsaffar²

¹Department of Information Security, College of Computer Science and Engineering, University of Ha'il, Ha'il, Saudi Arabia.

Frontiers in Artificial Intelligence

|June 17, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a novel method for detecting video splicing forgeries by analyzing spatial and compression domains. The technique achieves high accuracy, identifying manipulated regions using statistical clues and the VGG-16 model.

Keywords:

VGG16 deep learning statistical features video forensics video forgery

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Area of Science:

Digital Forensics
Computer Vision
Image Processing

Background:

Video splicing is a common forgery technique.
Existing methods for spatial domain analysis have limitations.
Detecting manipulated video content is crucial for authenticity verification.

Purpose of the Study:

To propose a new technique for detecting spliced video forgeries.
To leverage statistical clues from spatial and compression domains.
To enhance the accuracy of video forgery detection.

Main Methods:

A multi-feature architecture using the VGG-16 model is employed.
Statistical features are extracted from both spatial and compression domains (DCT).
The method analyzes fused characteristics in specific image regions for manipulation detection.

Main Results:

The proposed technique was tested on the HTVD and GRIP datasets.
Splicing forgery accuracy reached 93.50% on the GRIP dataset.
An accuracy of 92.40% was achieved on the HTVD dataset.

Conclusions:

The proposed method effectively detects spliced video forgeries.
Analyzing both spatial and compression domains provides robust detection capabilities.
The VGG-16 model aids in identifying subtle manipulation traces.