Image Splicing Forgery Detection using Error Level Analysis and CNN
The issue of image forgery through splicing has become increasingly relevant in the current digital era. Splicing involves the manipulation of images by combining parts of two or more different images to create a deceptive composite image. This technique can be employed for various purposes, including the dissemination of false information, damaging someone's reputation, or even creating confusion in specific contexts. Several techniques used to detect splicing involve statistical analysis, color analysis, and texture analysis. Additionally, artificial intelligence developments, such as deep learning, have been applied to enhance detection capabilities. In this study, we employed a Convolutional Neural Network (CNN) model to identify image deviations caused by splicing. Optimization was performed on the convolutional layers of the model to improve CNN performance. The integration of Error Level Analysis (ELA) was also implemented to aid in identifying splicing forgeries, where portions of one image are combined with parts of another. Areas that have undergone splicing may exhibit noticeable differences in error levels. The dataset utilized for this research was sourced from DVMM and CUISDE. The validation accuracy results for our CNN model before incorporating ELA were 61% for DVMM and 74% for CUISDE. After adding ELA, the CNN model demonstrated improved detection accuracy, achieving validation rates of 72% for DVMM and 71% for CUISDE.
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