Copy-move image forgery detection based on SIFT descriptors and SVD-matching

In recent years, more data are created in digital form allowing easy control over storage and manipulation due to the technology progress. Unfortunately, this progress may have dragged along a lot of risks, especially the ones related to the security of digital files. In particular, digital forgery becomes a worry for many organizations because it has become easier to create fake images without leaving any obvious perceptual traces of tampering. A specific form of image forgery operation called “copy-move” is considered one of the most difficult problems in the case of forgery detection. For this case, a part of the image is copied and pasted on another location of the same image to conceal undesirable objects in the scene. In this paper, we propose a method that automatically detects duplicated regions in the same image. Duplicated detection is performed by identifying the local characteristics of the images (points of interest) using the Scale Invariant Feature Transform (SIFT) method and by matching between identical features using the Singular Value Decomposition (SVD) method. Obtained results show that our proposed hybrid method is robust to geometrical transformations and is able to detect with high performance duplicated regions.