Forged Copy-Move Recognition Using Convolutional Neural Network

Abstract

Due to the extreme robust image editing techniques, digital images are subject to multiple manipulations and decreased costs for digital camera and smart phones. Therefore, image credibility is becoming questionable, specifically when images have strong value, such as news report and insurance claims in a crime court. Therefore, image forensic methods test the integrity of the images by applying various highly technical methods set out in the literature. The present work deals with one important research module is the recognition of forged part that applied on copy move forgery images. Two datasets MICC-F2000 and CoMoFoD are used, these datasets are usually adopted in the field of interest. The module concerned with recognizing which is the source image portion and which is the target one of that already detected. Thus, the two detected tampered parts of the image are recognized the original one from them, the other is then referred as forged or tampered part. The proposed module used the buster net of three neural networks that basically adopted the principle of training by using Convolution Neural Network (CNN) to extract the most important features in the images. The first and second networks are parallel working to detect and identify areas that have been tampered with, and then display them through two masks. While the last network classifier takes a copy of these two catchers to decide which is the source image portion from the two detected ones. The achieved recognition results were about F-score 98.98% even if the forged area is rotated or scaled or both of them. Also, the recognition results of the forged image part was 98% when using images do not contributed in the training phase, which refers to that the proposed module is more confident and reliable.

Image Copy-move Forgery Detection and Classification Using Golden Jackal Optimization Based Multi-Support Vector Machine

“…In this state, the exploration phase is occurring, and the global jackals hunting action is to select the male (𝑋 𝑚𝑎𝑙𝑒 ) as the leader and the female (𝑋 𝑓𝑒𝑚𝑎𝑙𝑒 ) as the adherent is represented in Eqs. ( 7) and (8),…”

“…This type of forgery is popular because there is a chance that copied portion of an image has the same content, texture and features [7]. There are different traditional techniques for image forgery detection, which mostly include key-points-based and blockbased feature extraction and matching techniques [8]. Deep learning-based techniques are introduced for overcoming these issues of digital image forgery [9].…”

A Review on Copy Move Forgery Detection in Document Images

“…In block-based methods image is divided in blocks of fixed dimension and further features are extracted corresponding to each block of image. Ayat fadel et al [25] suggested module used the buster net of three neural networks that basically adopted the principle of training by using Convolution Neural Network (CNN) to extract the most important features in the images. The first and second networks are parallel working to detect and identify areas that have been tampered with, and then display them through two masks.…”