Dijana Tralic

Combining cellular automata and local binary patterns for copy-move forgery detection

Detection of duplicated regions in digital images has been a highly investigated field in recent years since the editing of digital images has been notably simplified by the development of advanced image processing tools. In this paper, we present a new method that combines Cellular Automata (CA) and Local Binary Patterns (LBP) to extract feature vectors for the purpose of detection of duplicated regions. The combination of CA and LBP allows a simple and reduced description of texture in the form of CA rules that represents local changes in pixel luminance values. The importance of CA lies in the fact that a very simple set of rules can be used to describe complex textures, while LBP, applied locally, allows efficient binary representation. CA rules are formed on a circular neighborhood, resulting in insensitivity to rotation of duplicated regions. Additionally, a new search method is applied to select the nearest neighbors and determine duplicated blocks. In comparison with similar methods, the proposed method showed good performance in the case of plain/multiple copy-move forgeries and rotation/scaling of duplicated regions, as well as robustness to post-processing methods such as blurring, addition of noise and JPEG compression. An important advantage of the proposed method is its low computational complexity and simplicity of its feature vector representation.

Video frame copy-move forgery detection based on Cellular Automata and Local Binary Patterns

Copy-move forgery (CMF) is a common image forgery method that implies copying and moving a part of image to a new location in the same image. In video sequences, CMF can be accomplished by copying a set of frames and pasting them to a new location in the same sequence. The result of this process is usually changing of video content. To identify video CMF, it is necessary to develop a robust descriptor for identification of duplicated video frames. This paper presents a novel method where Cellular Automata (CA) and Local Binary Patterns (LBPs) are used as texture descriptors. The main idea is to divide every frame into overlapping blocks and use CA to learn a set of rules for every block in a frame. Those rules appropriately describe the intensity changes in every block so their histogram can be used as a feature for detection of duplicated frames. Experimental testing showed a good performance of a proposed method for detection of video CMF in all tested cases.

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.

Wireless technologies for position awareness

Wireless communication technologies use the radio waves to transfer information between two or more devices that communicate. Propagation of electromagnetic waves through free space can be used for communication or for positioning and navigation. One of the basic principles of the device for positioning, which works on the basis of receiving and transmitting radio waves, is the measurement of time of propagation of electromagnetic waves. Device transmits electromagnetic pulse of certain strength and duration in the free space, on the basis of the reflected echo of the same pulse (e.g. time delay, signal strength) the parameters of the target are determined. Parameters of target may be its distance, azimuth, elevation, and information on the movement of the target (speed, direction of movement). In some cases it is possible to determine the type of target, for example, what kind of flying objects is moving. In general, it can be said that the problem of radiolocation can be reduced to measurement of the distance between multiple wireless terminals participating in the position measurement. The need for the location of wireless terminals is growing due to the large number of applications that benefit from such information (military purposes, industry, medicine, commercial services).

Virtual wireless penetration testing laboratory model

Penetration testing is one of the key actions in ensuring information system security and thereby avoiding security incidents including eavesdropping or unauthorized access to internal systems. In traditional educational settings it can be difficult to provide training for practitioners to develop and practice wireless penetration testing techniques, due to the requirement for physical equipment which can be used only by one person at the time, the need for student to be present at the location of the lab and the need to have expert in the lab to assess students progress. A virtual wireless penetration testing laboratory designed for educational purposes could overcome these problems. In this paper we propose a model of such a laboratory.