**8. Discussion and conclusions**

**Figure 10.** Example images of the reference group (left) and the test group (right). Note the difference in perspective

**Figure 11.** Left to right and from top to bottom: Frequency of Walls-Graffiti (perspective change), Leuven (illumination

change), and Boats (magnification and rotation).

and colors.

44 Evolving BCI Therapy - Engaging Brain State Dynamics

A brain-computer interface (BCI) is a direct interface between the human brain and an artificial system. Its purpose is to control the actuation of a device.

Many researchers have proposed modern algorithms to solve the problem of image authentication. This study explored and compared the application of different algorithms that detect common types of image forgery. The characteristics of the algorithms are shown in **Table 2**. The algorithms we examined in this study are undoubtedly important for the detection of image counterfeiting. Previous researchers have attempted to improve the reliability of image fraud detection algorithms. They have achieved this objective by (1) reducing algorithm complexity and computational time. This objective was achieved by using small vector dimensions, as shown in Refs. [18, 37–41] increasing the robustness of the algorithms. This aim was achieved by adopting a powerful feature that is consistent for a wide range of image processes, as shown in Refs. [42–48]. The algorithm based on fixed key indicators and fixed instances exhibits remarkable performance, as shown in **Table 2**. However, several barriers and challenges remain. We summarize the defects of available algorithms in **Tables 1** and **2**: (1) the algorithms cannot handle all possible types of image processing that can be applied to forge images; (2) some algorithms rely heavily on several threshold elements or initial value, and the identification of these threshold elements and values require experimentation and improvements; and (3) most current methods take time [49, 50]. The development of complex and reliable algorithms that quickly and rapidly detect image forgery has been proposed. However, future work must overcome the following challenges: (1) the lack of standardized datasets for false counterfeiting limits the comparability and reproduction of existing algorithms, as well the design of improved algorithms and (2) the lack of common quantitative methods for measuring and evaluating algorithm performance prevents the comparison of different algorithms under different conditions. We believe that this reason accounts for the absence of studies that compare the accuracy and performance of different algorithms. Given that detecting counterfeiting is still in its early stages, considerable work remains to be performed, and other ideas can be derived or borrowed from other fields, such as object recognition or image analysis (**Figure 12**).

Rotation Invariant on Harris Interest Points for Exposing Image Region Duplication Forgery

http://dx.doi.org/10.5772/intechopen.76332

47

and Fitian Ajeel<sup>2</sup>

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1 Information of Computer Science and Information Technology, University of Malaysia,

2 University of Information Technology and Communication, Baghdad, Iraq

**Author details**

Haitham Hasan Badi<sup>1</sup>

Kuala Lumpur, Malaysia

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Heidelberg: Springer; 2007. pp. 57-59

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\*, Bassam Sabbri<sup>2</sup>

\*Address all correspondence to: haitham@uoitc.edu.iq

**Figure 12.** Left to right and top to bottom: graphs of changes in 50 (Walls) grades, descale element 2 (Boats), image blur (Bikes and Trees), illumination level (Leuven), and JPEG compression (Ubc).

the accuracy and performance of different algorithms. Given that detecting counterfeiting is still in its early stages, considerable work remains to be performed, and other ideas can be derived or borrowed from other fields, such as object recognition or image analysis (**Figure 12**).
