Image Forgery Detection
Image Forgery Detection
ABSTRACT:
Image forgery means manipulation of digital image to conceal meaningful information of the image. The detection of forged image is driven by the need of authenticity and to maintain integrity of the image. A copy move forgery detection theme victimization adaptive over segmentation and have purpose feature matching is proposed. The proposed scheme integrates both block based and key point based forgery detection methods. The proposed adaptive over segmentation algorithm segments the host image into non over lapping and irregular blocks adaptively. Then, the feature points are extracted from each block as block features, and the block features are matched with one another to locate the labeled feature points; this procedure can approximately indicate the suspected forgery regions. To detect the forgery regions more accurately, we propose the forgery region extraction algorithm which replaces the features point with small super pixels as feature blocks and them merges the neighboring blocks that have similar local color features into the feature block to generate the merged regions. Finally, it applies the morphological operation to merged regions to generate the detected forgery regions. In cut paste image forgery detection, proposed digital image forensic techniques capable of detecting global and local contrast enhancement, identifying the use of histogram equalization.
PROJECT OUTPUT VIDEO:
MODULES:
-
Feature Extraction for Original Image
-
Forgery Region Detection
MODULE DESCRIPTION:
1. Feature Extraction for Original Image:
IMAGE ACQUISITION:
-
Image acquisition in image processing can be broadly defined as the action of retrieving an image from some source, usually a hardware-based source, so it can be passed through whatever processes need to occur afterward.
-
Performing image acquisition in image processing is always the first step in the workflow sequence because, without an image, no processing is possible. The image that is acquired is completely unprocessed and is the result of whatever hardware was used to generate it, which can be very important in some fields to have a consistent baseline from which to work.
-
Original image acquired from gallery. After that, original image converted into gray image for further process.
OBJECT DETECTION:
-
In this step, thresholding and morphological operation is used for object detection.
i. Thresholding Method:
-
Grayscale thresholding is employed to segment the nodule in the CT image. Binary image is called as thresholding image.
-
Thresholding makes it possible to highlight pixels in an image. Thresholding can be applied to gray scale images or color images. In this discussion gray scale images are used. In Thresholding a pixel intensity value is adjusted, by taking the given value as reference the low intensity pixels will become zero and rest of the pixels will become 1. The result of the Thresholding is a binary image containing black and white pixels.
ii. Morphological Operation:
-
It is used for remove the small object in segmented image. Finally object was detected.
FEATURE EXTRACTION:
-
In feature extraction, we used SIFT features for forgery detection.
-
We first detect key points on the image based on the DOG detector. Afterwards, the SIFT descriptor is chosen for key point description. The combination of DOG detector and SIFT descriptor has been shown to outperform other detector & descriptor combinations in many applications.
2. Forgery Region Detection:
-
In this step, forgery image was loaded. Then, it was converted into gray image.
-
After, SIFT feature extraction was performed in gray image.
-
Finally, based on the matched key points forgery regions and tampering percentage are identified.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
-
System : Pentium Dual Core.
-
Hard Disk : 120 GB.
-
Monitor : 15’’ LED
-
Input Devices : Keyboard, Mouse
-
Ram : 1GB.
SOFTWARE REQUIREMENTS:
-
Operating system : Windows 7.
-
Coding Language : MATLAB.
-
Tool : MATLAB R2013A /2018
