Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3937 2019 2019 National School of Electronics and Telecoms of Sfax, University of Sfax, Tunisia; Al-Nahrain University, Baghdad, Iraq Mohammed Mohammed National School of Computer Science (ENSI), University of Manouba, Tunisia Mohamed Elleuch Ministry of Education, Wasit Education Directorate, Iraq Monji kherallah Faculty of Sciences of Sfax, University of Sfax, Tunisia Faiza Charfi 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. 2026 2026 103 113 10.54216/JISIoT.180107 https://www.americaspg.com/articleinfo/18/show/3937