Dbelm for Image Forgery Detection  
  Authors : Arun Anoop M; Poonkuntran S


Image forgery or manipulation is the removal of interested region from the particular image by the use of freely available manipulation tools. One of the popular attacks is copy move forgery attack. Main aim of image forgery is to hide the interested region or reproducing new region in that particular image in order to falsify the particular image for publicity or saving their individual from danger or fame or earns or cheat or fun An 'Alexnet based Convolutional Neural Network(CNN)' is intended for picture fraud discovery. As the accuracy parameters resulted same in the case of one attack consideration in previous methodologies, this framework concentrate copy move attack and splicing attacks. The experiment results proposed DBELM outperforms existing CMFD frameworks by a significant edge on the five freely available datasets: MIASDBv1, CASIA, CoMoFoD, Kodak & Google and MNIST handwritten dataset. For primer evaluation, MNIST dataset initially processed to check classification accuracy by CNN based on different splits and not processed by DBELM. That motivation helped us to take 90:10 split for DBELM research work. Novelty of the work is the design and implementation of proposed system, and moreover the review of previous algorithm(s) in the area of feature extraction, which aims to explore the different extraction methods, feature determination which is utilized for dimensionality reduction to eliminate repetitive and immaterial features, classifiers which are utilized for supervised or unsupervised machine learning and metrics which are the measures or parameters or accuracy improving methods for evaluation. A small % of accuracy improvement will help to cure patient's health from danger if the important document is altered. And also we located the forged region with the help of moment based algorithm (Zernike Moment) and used bat optimized extreme learning machine for better image forgery classification accuracy. An accuracy of 98% has been accomplished for the four datasets. Proposed framework demonstrates the credibility of the digital image from forgery. For better evaluation, review about all the related terms, comparative results and performance evaluation results are included. And the DBELM outperforms our previous methodologies LORA and LPG. In future, DBELM will process MNIST handwritten dataset and compare with DELM-AE, DELM & DBP methods and find out the best one among those for image forgery detection research field.


Published In : IJCAT Journal Volume 7, Issue 10

Date of Publication : October 2020

Pages : 151-160

Figures :16

Tables :06




Arun Anoop M : PhD Scholar, Department of Computer Science and Engineering Velammal College of Engineering & Technology Madurai, Tamilnadu, India.

Poonkuntran S : Professor, Department of Computer Science and Engineering Velammal College of Engineering & Technology Madurai, Tamilnadu, India.








Image forgery and detection; BAT ecology algorithm(BAT); Extreme Learning Machine(ELM); Alexnet based Deep CNN; Copy Move Forgery Detection(CMFD)















Authenticity expectation of a picture has ended up being basic these days in each field. Trial results have indicated that this technique gives high classification accuracy regarding various splits as we considered all through our exploration work. In the tests, the proposed technique beats some current strategies for our past exploration approaches and furthermore conventional techniques. Among four classifiers ELM demonstrated as productive in work with high classification accuracy rate. With BATELM algorithm, accomplished 98%. In future, we will do likewise with some auto-encoders ideas with various epochs.










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