Image splicing forgery detection: A review

Kumari, Ritesh; Garg, Hitendra

doi:10.1007/s11042-024-18801-z

Cited by 2 publications

(2 citation statements)

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“…On the other hand, recall measures the algorithm's effectiveness in appropriately detecting the pixels that belong to the tampered area. This metric highlights the system's ability to identify manipulated areas, as given in Equation (7).…”

Section: 𝑎𝑐𝑐 = 𝐶𝑜𝑟𝑟𝑒𝑐𝑡 𝑝𝑖𝑥𝑒𝑙𝑠 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 𝑇𝑜𝑡𝑎𝑙 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛𝑠mentioning

confidence: 99%

“…The image splicing technique involves duplicating an image region into another image, which has garnered significant attention in image forensics. The detection of this type of forgery has become a relevant area in guaranteeing image authenticity [5][6][7]. However, pattern recognition is essential in image forensics, particularly for detecting splicing manipulations within images.…”

Section: Introductionmentioning

confidence: 99%

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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: 𝑎𝑐𝑐 = 𝐶𝑜𝑟𝑟𝑒𝑐𝑡 𝑝𝑖𝑥𝑒𝑙𝑠 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 𝑇𝑜𝑡𝑎𝑙 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛𝑠mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%