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Amandeep Kaur
Isha Vats


With the current development in technology, image manipulation and particularly image splicing has been termed as a regular and established concern. The swift growth in commercial image editing programs and software for instance Adobe Photoshop has significantly elevated the number of forged, doctored and tampered images on a daily basis. This situation has resulted into extreme consequences, minimizing the authenticity and reliability of such images as well as creating untrue beliefs in a wide range of ideal-world relevance. The authenticity of a digital image suffers from severe threats due to the rise of powerful digital image editing tools that easily alter the image contents without leaving any visible traces of such changes. The splicing forgery can be done by copied a one/more region from source image and pasted into a target image to produce a composite image called spliced image. Therefore, this type of forgery is considered very challenging from tampering detection point of view. To make the matter worst some post-processing effects such as blurring, JPEG compression, rotation and scaling maybe introduced in the spliced image. In previous paper, we study found the problems in forgery detection in Jpg images. Robust feature extraction techniques used in block based copy move image forgery detection require a high computation time. There is a need for reduced computation time for detection schemes to be practical for use with large images and in real time environments. Utilization of GPU to compute the processes can highly parallelize the tasks involved to reduce computation time. Current CPU based algorithms that have been designed are not suitable to be directly adopted in a GPU based scheme. It must be determined how a parallel copy move image forgery detection scheme can be designed for use with a GPU. Digital images provide a new way to represent pictures and scenes that only film and a darkroom could supply before. In this research work, we implement the feature extraction using principle component analysis and optimization (ant colony optimization) algorithm to detect the forgery image in JPG images. In optimization approach to classify the features and match the training feature if training and testing features has matching then detect the forgery image in the jpg images. Evaluate the performance parameters PSNR (Peak Signal to Noise Ratio), Error rate Accuracy and compare the existing parameters i.e accuracy.
Keywords- Image Forgery, Robust feature extraction, Ant colony Approach and Neural Network


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Author Biography

Amandeep Kaur, PTU

Department of Computer Science and Engineering


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