A New Efficient Forgery Detection Method using Scaling, Binning, Noise Measuring Techniques and Artificial Intelligence (Ai)

Main Article Content

Mahesh Enumula
Dr. M. Giri
Dr. V. K. Sharma

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.

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How to Cite
[1]
Mahesh Enumula, Dr. M. Giri, and Dr. V. K. Sharma , Trans., “A New Efficient Forgery Detection Method using Scaling, Binning, Noise Measuring Techniques and Artificial Intelligence (Ai)”, IJITEE, vol. 12, no. 9, pp. 17–21, Aug. 2023, doi: 10.35940/ijitee.I9703.0812923.
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Author Biographies

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

Mahesh Enumula is currently pursuing Ph.D in Bhagwanth University, Ajmer, Rajasthan, India on the research topic Image forgery detection using Artificial Intelligence. He did Bachelors and Masters in Technology on Electronics and Communication Engineering from JNTU, Andhra Pradesh, India. He is holding a patent on Image forgery detection topic from the Government of Australia. Apart from Artificial Intelligence his interests are Embedded systems and VLSI Design. He is having 9 international journal papers and 4 conference papers.

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

Dr. M. Giri Professor, Department of CSE, Siddharth Institute of Engineering and Technology, Puttur. He did his B.Tech in Computer Science & Engineering from Sree Vidya Nikethan Engineering College, Tirupati, affiliated to JNTU, Hyderabad, in 2001. He did his M.Tech in Computer Science & Engineering from School of IT, JNTU Hyderabad campus, Hyderabad in 2009. He did his Ph.D in Computer Science & Engineering from Raalaseema University, Kurnool, in 2018. He is having 22 years of teaching experience. He organized and participated in various Workshops, FDPs, Seminars in different areas of Computer Science during his tenure. He has published 68 papers in various reputed International/National journals and Conferences. He is a member of IEEE, MCSIT, MIAENG and MCSTA. His research area includes Data Mining, Wireless Sensor Networks, Artificial Intelligence, Cryptography, Network Security, Cloud Computing and IoT.

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

Dr. V. K. Sharma received his B.E. degree in Electrical Engineering from KREC (NIT), Surathkal, India in 1984 and received his M.Tech degree in Power Electronics from IIT Delhi, India in 1993. He received his Ph.D. degree in the field of Electric Drives from IIT Delhi, India in 2000 and he has done one year stint as Post-Doctoral Fellow in Active Filters from ETS, Montreal Canadain 2001. Presently, he is a Vice-Chancellor of Bhagwant University, Ajmer, India and he is also Professor in the department of EEE since 2014. He is having total 36 years of teaching experience. He has authored or co-authored over more than 200 papers in various SCI, SCOPUS Indexed and other national, international journals. He completed major projects sponsored by public funding agencies like AICTE, DST etc. He received various awards like Railway Board Medal, Lions Award, and UGC Research Associate etc. His research interests include Electric Drives, Active Filters, Antennas and Renewable energy conversion techniques. He is a senior member of IEEE, Fellow IETE and Member IE (I).

How to Cite

[1]
Mahesh Enumula, Dr. M. Giri, and Dr. V. K. Sharma , Trans., “A New Efficient Forgery Detection Method using Scaling, Binning, Noise Measuring Techniques and Artificial Intelligence (Ai)”, IJITEE, vol. 12, no. 9, pp. 17–21, Aug. 2023, doi: 10.35940/ijitee.I9703.0812923.
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References

B. P. Das, M. Biswal, A. Panigrahi, M. Okade, “CNN Based Image Resizing Detection and Resize Factor Classification for Forensic Applications”, 2021 2nd International Conference on Range Technology (ICORT), pp. 1-6, 2021.

F. Marra, D. Gragnaniello, L. Verdoliva, G. Poggi, “A Full Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection”, IEEE Access, vol. 8, pp. 133488-133502, 2020. https://doi.org/10.1109/ACCESS.2020.3009877

K. H. Rhee, “Detection of Spliced Image Forensics Using Texture Analysis of Median Filter Residual”, IEEE Access, vol. 8, pp. 103374-103384, 2020. https://doi.org/10.1109/ACCESS.2020.2999308

C. Wang, Z. Zhang, Q. Li, X. Zhou, “An Image Copy-Move Forgery Detection Method Based on SURF and PCET”, IEEE Access, vol. 7, pp. 170032-170047, 2019. https://doi.org/10.1109/ACCESS.2019.2955308

D. Wang, T. Gao, Y. Zhang, “Image Sharpening Detection Based on Difference Sets”, IEEE Access, vol. 8, pp. 51431-51445, 2020. https://doi.org/10.1109/ACCESS.2020.2980774

X. Lin, C.T. Li, “PRNU-Based Content Forgery Localization Augmented With Image Segmentation”, IEEE Access, vol. 8, pp. 222645-222659, 2020. https://doi.org/10.1109/ACCESS.2020.3042780

S. Luo, A. Peng, H. Zeng, X. Kang, L. Liu, “Deep Residual Learning Using Data Augmentation for Median Filtering Forensics of Digital Images”, IEEE Access, vol. 7, pp. 80614-80621, 2019. https://doi.org/10.1109/ACCESS.2019.2923000

A. Peng, S. Luo, H. Zeng, Y. Wu, “Median Filtering Forensics Using Multiple Models in Residual Domain”, IEEE Access, vol. 7, pp. 28525-28538, 2019. https://doi.org/10.1109/ACCESS.2019.2897761

Q. Yin, J. Wang, X. Luo, J. Zhai, S. K. Jha, Y. Q. Shi, “Quaternion Convolution Neural Network for Color Image Classification and Forensics”, IEEE Access, vol. 7, pp. 20293-20301, 2019. https://doi.org/10.1109/ACCESS.2019.2897000

K. T. Ahmed, S. Jaffar, M. G. Hussain, S. Fareed, A. Mehmood, G. S. Choi, “Maximum Response Deep Learning Using Markov Retinal & Primitive Patch Binding With GoogLeNet & VGG-19 for Large Image Retrieval”, IEEE Access, vol. 9, pp. 41934-41957, 2021. https://doi.org/10.1109/ACCESS.2021.3063545

Z. J. Barad, M. M. Goswami, “Image Forgery Detection using Deep Learning: A Survey”, 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2020, pp. 571-576. https://doi.org/10.1109/ICACCS48705.2020.9074408

W. Wang et al., “Anomaly detection of industrial control systems based on transfer learning”, Tsinghua Science and Technology, vol. 26, no. 6, pp. 821-832, Dec. 2021. https://doi.org/10.26599/TST.2020.9010041

G. Boato, D. Dang-Nguyen, F.G.B. De Natale, “Morphological Filter Detector for Image Forensics Applications”, IEEE Access, vol. 8, pp. 13549-13560, 2020. https://doi.org/10.1109/ACCESS.2020.2965745

Jing Dong, Wei Wang, Tieniu Tan, “CASIA Image Tampering Detection Evaluation Database”, IEEE China Summit and International Conference on Signal and Information Processing, 2013. https://doi.org/10.1109/ChinaSIP.2013.6625374

B. V. Somasundaran, R. Soundararajan, S. Biswas, “Image Denoising for Image Retrieval by Cascading a Deep Quality Assessment Network”, 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 525-529, 2018. https://doi.org/10.1109/ICIP.2018.8451132

M. Giri, S.Jyothi, “Big Data Collection and Correlation Analysis of Wireless Sensor Networks Yielding to Target Detection and Classification”, Springer Lecture Notes on Data Engineering and Communications Technologies, Vol. 9, ISSN: 2367-4512, print ISBN: 978-981-10-6318-3, pp. 201-213, 2017. https://doi.org/10.1007/978-981-10-6319-0_18.

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