2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 1 , PP: 103-113, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Mohammed S. Khazaal 1 * , Mohamed Elleuch 2 , Monji kherallah 3 , Faiza Charfi 4
Doi: https://doi.org/10.54216/JISIoT.180107
Currently, images stand for a highly common form of communication, whether through teleconferencing, mobile communication or social media. The identification of counterfeit images is intrinsic because it is crucial that the images used for communication be genuine and original. Images are fabricated referring to the fact that it is challenging to set the difference between a tampered image and the real image. This refers notably to the myriad technological, moral, and judicial implications connected with advanced image editing software. The majority of handcrafted traits are used in traditional approaches for detecting image counterfeiting. The problem with many of the image tampering detection methods now in use resides in the fact that they are confined to identifying particular types of alteration by looking for particular features in the images. Image tampering is currently recognized through deep learning techniques. These methods proved to be promising and worthwhile as they perform better than traditional ones since they can extract complex components from images. As far as this research paper is concerned, we provide a thorough review of deep learning-based methods for detecting splicing images, along with the pertinent results of our survey in the form of findings and analysis.
Deep learning , Image tampering , Splicing , Image splicing detection
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