engleski

Copy-Move Forgery Detection Using Cellular Automata

Thanks to the availability of many sophisticated image processing tools, digital image forgery is prevalent nowadays. One of the common methods is copymove forgery (CMF), where part of an image is copied to another location in the same image. Detection of copy-move forgery has been widely researched recently, and many different solutions have been proposed. This chapter introduces a different approach, in which cellular automata (CA) are applied to the task of copy-move forgery detection (CMFD). The basic idea is to learn, for each overlapping block in the image, a set of CA rules that represents the intensity changes within that block. These rules are then used as features for the detection of copied blocks. A problem arises when applying CA to image processing. If pixel intensities are used as cell states, then the large range of image intensities leads to a combinatorial explosion in the number of possible rules, making it difficult to both learn and represent rules efficiently.We describe a solution in which a reduced description of a neighbourhood is accomplished by a proper binary representation of the image based on local binary patterns (LBPs). In the case of plain copy-move forgery, a simple 1D CA are sufficient for detection purposes, but any transformation of the copied area (for example, rotation and scaling) introduces large changes into the binary representation of the image, resulting in the need for more complicated forms of the CAâ˘A ´ Zs neighbourhood. However, the main issue of CMFD using CA rules is its sensitivity to processing after the copy-move operation applied to hide traces of the forgery, for example, addition of noise. Nevertheless, in some cases the CA can effectively cope with such forgeries if image pre-processing (for example, simple image filtering) is applied before forgery detection.

Cellular automata, Local Binary Pattern, copy-move forgery

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