Published August 30, 2023 | Version CC BY-NC-ND 4.0
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A New Efficient Forgery Detection Method using Scaling, Binning, Noise Measuring Techniques and Artificial Intelligence (Ai)

Creators

  • 1. Research Scholar, Department of Electrical Communication Engineering, Bhagwant University, Ajmer (Rajasthan), India.
  • 2. Professor, Department of Computer Science and Engineering, Siddharth Institute of Engineering and Technology, Puttur (Karnataka), India.
  • 3. Professor, Department of Electrical Communication Engineering, Bhagwant University, Ajmer (Rajasthan), India

Contributors

Contact person:

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

Description

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.

Notes

Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved.

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Journal article: 2278-3075 (ISSN)

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

Subjects

ISSN: 2278-3075 (Online)
https://portal.issn.org/resource/ISSN/2278-3075#
Retrieval Number: 100.1/ijitee.I97030812923
https://www.ijitee.org/portfolio-item/I97030812923/
Journal Website: www.ijitee.org
https://www.ijitee.org/
Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
https://www.blueeyesintelligence.org/