Abstract
As digital images become an indispensable source of information, the authentication of digital images has become crucial. Various techniques of forgery have come into existence, intrusive, and non-intrusive. Image forgery detection hence is becoming more challenging by the day, due to the unwavering advances in image processing. Therefore, image forensics is at the forefront of security applications aiming at restoring trust and acceptance in digital media by exposing counterfeiting methods. The proposed work compares between various feature selection algorithms for the detection of image forgery in tampered images. Several features are extracted from normal and spliced images using spatial grey level dependence method and many more. Support vector machine and Twin SVM has been used for classification. A very difficult problem in classification techniques is to pick features to distinguish between classes. Furthermore, The feature optimization problem is addressed using a genetic algorithm (GA) as a search method. At last, classical sequential methods and floating search algorithm are compared against the genetic approach in terms of the best recognition rate achieved and the optimal number of features.
Highlights
In today's era of digitalization and computerization, information is mostly conveyed through digital images and videos
Due to the increase in the availability of a wide range of image-editing software and advancement in processing techniques, image forgery is on the rise and thereby effecting diverse areas of life, such as politics, cybercrime investigations, medical diagnoses based on images, politics, businesses etc
The same set of feature set belonging to MICC F220 and Columbia dataset when applied to classical feature selection algorithms like Sequential floating forward selection (SFFS), sequential backward selection (SBS), sequential forward selection (SFS), and like methods gave the following feature subset with the accuracy as shown below in table 7 and table 8: Table 7
Summary
In today's era of digitalization and computerization, information is mostly conveyed through digital images and videos. Due to the increase in the availability of a wide range of image-editing software and advancement in processing techniques, image forgery is on the rise and thereby effecting diverse areas of life, such as politics, cybercrime investigations, medical diagnoses based on images, politics, businesses etc Crimes, such as defaming of websites, personalities etc., are on the rise and make use of such tools to do the act. The available techniques for tampering detection can be divided into broadly into active approaches [1][2] and passive approaches [3][4] The former needs addition of traces of authenticity to be able to detect later for any manipulations, for example a digital watermark where we feed some information prior to image sharing and the detection can be done based on modifications to that information, whereas the passive approaches use a blind approach, perform the same task without any prior information. The proposed method uses a blind approach to find out whether the given image is altered or not
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