Image splicing forgery detection using noise level estimation

Meena, Kunj Bihari; Tyagi, Vipin

doi:10.1007/s11042-021-11483-x

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…These techniques were employed to remove correlated features, and the SVM was utilized to determine manipulated image areas. Meena and Tyagi [15] estimated the noise level for each block using the SLIC algorithm. Then, they applied the K-means algorithm to cluster similar regions based on this noise level estimation.…”

Section: Previously Reported Algorithmsmentioning

confidence: 99%

AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks

Ramirez-Rodriguez,

Arevalo-Ancona,

Perez-Meana

et al. 2024

Applied Sciences

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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.

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Section: Previously Reported Algorithmsmentioning

confidence: 99%