Digital image forgery is a serious problem of an increasing attention from the research society. Image splicing is a well-known
type of digital image forgery in which the forged image is synthesized from two or more images. Splicing forgery detection is
more challenging when compared with other forgery types because the forged image does not contain any duplicated regions.
In addition, unavailability of source images introduces no evidence about the forgery process. In this study, an automated image
splicing forgery detection scheme is presented. It depends on extracting the feature of images based on the analysis of color filter
array (CFA). A feature reduction process is performed using principal component analysis (PCA) to reduce the dimensionality of
the resulting feature vectors. A deep belief network-based classifier is built and trained to classify the tested images as authentic
or spliced images. The proposed scheme is evaluated through a set of experiments on Columbia Image Splicing Detection
Evaluation Dataset (CISDED) under different scenarios including adding post-processing on the spliced images such JPEG
compression and Gaussian Noise. The obtained results reveal that the proposed scheme exhibits a promising performance with
95.05% precision, 94.05% recall, 94.05% true positive rate, and 98.197% accuracy. Moreover, the obtained results show the
superiority of the proposed scheme compared to other recent splicing detection method. |
