A New Efficient Forgery Detection Method using Scaling, Binning, Noise Measuring Techniques and Artificial Intelligence (Ai)
Mahesh Enumula1, M.Giri2, V.K. Sharma3

1Mahesh Enumula, Research Scholar, Department of Electrical Communication Engineering, Bhagwant University, Ajmer (Rajasthan), India.

2Dr. M. Giri, Professor, Department of Computer Science and Engineering, Siddharth Institute of Engineering and Technology, Puttur (Karnataka), India.

3Dr. V. K. Sharma, Professor, Department of Electrical Communication Engineering, Bhagwant University, Ajmer (Rajasthan), India. 

Manuscript received on 18 July 2023 | Revised Manuscript received on 05 August 2023 | Manuscript Accepted on 15 August 2023 | Manuscript published on 30 August 2023 | PP: 17-21 | Volume-12 Issue-9, August 2023 | Retrieval Number: 100.1/ijitee.I97030812923 | DOI: 10.35940/ijitee.I9703.0812923

Open Access | Editorial and Publishing Policies | Cite | Zenodo | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In the market new updated editing tools and technologies are available to edit images and with help of these tools images are easily forged. In this research paper we proposed new forgery detection technique with estimation of noise on various scale of input image affect of noise in input image, frequency of images are also changed due to noise, noise signal correlated with original input images and in compressed images quantization level frequency also changed due to noise.We partition input image into M X N blocks, resized blocks are proceed further, image colors are also taken into consideration, each block noise value is evaluated at local level and global level. For each color channel of input image estimate local and global noise levels are estimated and compared using binning method. Also measured heat map of each block and each color channel of input image and all these values are stored in bins. Finally from all noise values calculate average mean value of noise, with these values decide whether input image is forgery or not, and performance of proposed method is compared with existing methods.

Keywords: Image Processing, Machine Learning, Retouching, Binning, Forgery, Water Marking.
Scope of the Article: Machine Learning