Automatic Machine Learning Forgery Detection Based On SVM Classifier

For decades powerful digital image editing software makes image modifi cations straightforward. In this paper analyze one of the most common form of photographic manipulation is known as Image Composition or Splicing. For that purpose a forgery detection method is used to exploits subtle inconsistencies in the color of the illumination of images. The technique (Machine Learning) is applicable to images containing two or more people. To achieving this concept, the information from physics (Chromaticity)-and statistical (texture and edge)-based illuminate estimators on image regions of similar images are taken. Then the extracted texture and edge-based features are provided to machinelearning approaches for Automatic Decision-Making. The Classification performance achieved by an SVM (Support Vector Machine) meta-fusion classifier. In machine learning of SVM is a supervised learning model with associated in learning algorithm. KeywordsColor constancy, illuminant color, image forensics, machine learning, spliced image detection, texture and edge descriptors. SVM Classifier. I.INTRODUCTION Image manipulation is another way to say editing photos and can add filters, remove redness, adjust the hue, and increase or decrease size. Image Composition (or Splicing) is one of the most common image manipulation operations. One such example is shown in Figure. 1, in which the girl on the right is inserted. This image shows a harmless manipulation case. It is a simple process that crops and paste region from the same or separate sources. Color has been successfully used for object tracking and recognition. However, the color of an object changes if the illuminant’s color changes. Color-based method search for inconsistencies in the interactions between object color and light color. Color constancy is ability to perceive colors of objects for invariant to the image.When assessing the authenticity of an image, forensic investigator uses all available sources of tampering evidence. Figure.1 image composition Forensics investigators use all available sources of tampering evidence. Tampering means International modification of products in a way that would make them harmful to the consumer.Figure.2. As we can see in parliament meeting, one people not attends but that people should attend the meeting. For investigating manipulated images the texture and edge descriptors placed an important role. The texture is an inherently non-local image property. It is extract texture information from illuminant maps. Edge is when an image is spliced, the statistics such edges likely to differ from original image. The characterization method for illuminant maps which explores edges attributes related to the illumination process. Figure 2. Illuminant map Figure 3. illuminant maps for an original image(top) and a spliced ge(bottom). Figure .3. shows an Example of illuminant map that directly shows an inconsistency.Figure.4.shows an Example of illuminant maps for an original image(top) and a spliced image(bottom). S.L.Jothilakshmi et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 3384-3388