Enhanced image‐splicing classification: A resilient and scale‐invariant approach utilizing edge‐weighted local texture features

The spread of image editing tools demonstrates how modern mixed‐media technology enables changes in digital images. Such easy access raises severe moral and legal concerns around the potential for malicious image editing. Overcoming this difficulty will need the development of innovative approaches...

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Vydané v:Journal of forensic sciences Ročník 70; číslo 6; s. 2297 - 2308
Hlavní autori: Akram, Arslan, Jaffar, Muhammad Arfan, Rashid, Javed, Boulaaras, Salah Mahmoud, Faheem, Muhammad
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Wiley Subscription Services, Inc 01.11.2025
Predmet:
Change detection
Changes
Chromaticity
Classification
Color
Discrete Wavelet Transform
Editing
Forensic science
Forgery
forgery detection
Frequencies
Histograms
Image manipulation
Imagery
local binary pattern
Mass media effects
Mass media images
multiscale analysis
noise inconsistency detection
pattern recognition
Photography
robust feature representation
support vector machine classifier
Support vector machines
texture analysis
Transformation
noise inconsistency detection
multiscale analysis
local binary pattern
support vector machine classifier
robust feature representation
discrete wavelet transform
forgery detection
pattern recognition
texture analysis
ISSN:0022-1198, 1556-4029, 1556-4029
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Shrnutí:The spread of image editing tools demonstrates how modern mixed‐media technology enables changes in digital images. Such easy access raises severe moral and legal concerns around the potential for malicious image editing. Overcoming this difficulty will need the development of innovative approaches for the quick detection of changes in high‐quality photographs. This paper proposes a new way to solve this problem by analyzing chrominance discontinuities in spliced regions, DWT, and unique histograms based on local binary patterns. To start extracting the luminance and chrominance components, we change the input image's color space from RGB to YCBCR. Then, using discrete wavelet transformation, the blue and red chromaticity levels were converted into wavelet bands. We compute histograms using the CB and CR DWT high‐frequency bands. The next step is to use feature fusion methods to merge the CB and CR feature vectors from each high‐frequency band after we change the histograms into vectors. Finally, we train a Support Vector Machine (SVM) using the combined color characteristics. A binary SVM trained to identify spliced images between original and spliced images has been produced. Improving upon existing methods, the proposed method achieved up to 98.49% accuracy on CASIA v1.0, outperforming existing benchmarks, that is, 97.33% on DVMM and 98.25% on Casiav2.0, thereby enhancing splicing forgery detection. This method contributes to media forensics by providing a reliable tool for detecting tampered images, which holds significant relevance in legal investigations and digital content authentication.
Bibliografia:ObjectType-Article-1
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ISSN:0022-1198
1556-4029
1556-4029
DOI:10.1111/1556-4029.70143