RDIFR: Robust Digital Image Forgery Recognition System for Image Splicing Using Deep Learning

Ali, Ali Waleed; Ahmed, Ibrahim Nezar; Mahmoud, Amel Mohamed; El-Rabaie, El-Sayed; Abdel-Salam Bauomy, Nariman

doi:10.1109/jac-ecc61002.2023.10479638

2023 11th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) 2023

DOI: 10.1109/jac-ecc61002.2023.10479638

|View full text |Cite

RDIFR: Robust Digital Image Forgery Recognition System for Image Splicing Using Deep Learning

Ali Waleed Ali,

Ibrahim Nezar Ahmed,

Amel Mohamed Mahmoud

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks

Ramirez-Rodriguez,

Arevalo-Ancona,

Perez-Meana

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The exponential surge in specialized image editing software has intensified visual forgery, with splicing attacks emerging as a popular forgery technique. In this context, Siamese neural networks are a remarkable tool in pattern identification for detecting image manipulations. This paper introduces a deep learning approach for splicing detection based on a Siamese neural network tailored to identifying manipulated image regions. The Siamese neural network learns unique features of specific image areas and detects tampered regions through feature comparison. This architecture employs two identical branches with shared weights and image features to compare image blocks and identify tampered areas. Subsequently, a K-means algorithm is applied to identify similar centroids and determine the precise localization of duplicated regions in the image. The experimental results encompass various splicing attacks to assess effectiveness, demonstrating a high accuracy of 98.6% and a precision of 97.5% for splicing manipulation detection. This study presents an advanced splicing image forgery detection and localization algorithm, showcasing its efficacy through comprehensive experiments.

show abstract

AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks

Ramirez-Rodriguez,

Arevalo-Ancona,

Perez-Meana

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

RDIFR: Robust Digital Image Forgery Recognition System for Image Splicing Using Deep Learning

Cited by 1 publication

References 18 publications

AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks

AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks

Contact Info

Product

Resources

About