Journal of Scientific and Industrial Research

Open Access Open Access  Restricted Access Subscription Access

A Novel Approach to Detect Copy Move Forgery Using Deep Learning


Affiliations
1 Computer Engineering Department, J C Bose University of Science and Technology, YMCA, Faridabad 121 006, Haryana, India
 

With the development of readily available image editing tools, manipulating an image has become a universal issue. To check the authenticity, it is necessary to identify how various images might be forged and the way they might be detected using various forgery detection approaches. The importance of detecting copy-move forgery is that it identifies the integrity of an image, which helps in fraud detection at various places such as courtrooms, news reports. This article presents an appropriate technique to detect Copy-Move forgery in which to some extent an image is copied and pasted onto an equivalent image to hide some object or to make duplication. The input image is segmented using the real-time superpixel segmentation algorithm DBSCAN (Density based spatial clustering of application with noise). Due to the high accuracy rate of the VGGNet 16 architecture, it is utilized for feature extraction of segmented images, which will also enhance the efficiency of the overall technique while matching the extracted patches using adaptive patch matching algorithm. The experimental results reveal that the proposed deep learning-based architecture is more accurate in identifying the tempered area even when the images are noisy and can save computational time as compared to existing architectures. For future research, the technique can be enhanced to work on other forgery detection techniques such as image splicing and multi-cloned images.


Keywords

Adaptive patch matching, CNN, Copy move forgery detection, DBSCAN, VGGNet
User
Notifications
Font Size

  • Farid H, Image forgery detection, IEEE Signal Proces Mag, 26(2) (2009) 16–25.

  • Tyagi V, Understanding digital image processing, CRC Press (2018), ISBN978131512390.

  • Begum M & Uddin M S, Digital image watermarking techniques: A review, Information, 11(2) (2020) 110.

  • Evsutin O, Melman A & Meshcheryakov R, Digital steganography and watermarking for digital images: A review of current research directions, IEEE Access (2020) 166589–166611.

  • Teerakanok S & Uehara T, Copy-move forgery detection: A State-of-the-Art technical review and analysis, IEEE Access, 7 (2019) 40550–40568.

  • Warif N B, Wahab A W, Idris M Y, Ramli R, Salleh R B, Shamshirband S & Choo K, Copy-move forgery detection: Survey, challenges and future directions. J Netw Comput Appl, 75 (2016) 259–278.

  • Christlein V, Riess C, Jordan J & Angelopoulou E, An evaluation of popular copy-move forgery detection approaches, IEEE Trans Inf Forensics Secur, 7 (2012) 1–26.

  • Soni B, Das P K & Thounaojam D M, CMFD: A detailed review of block based and key feature-based techniques in image copy move forgery detection, IET Image Proc, 12(2) (2018) 167–178.

  • Zhang Z, Wang C & Zhou X, A survey on passive image copy move forgery detection, J Inf Process Syst, 14(1) (2018) 6–31.

  • Meena K B & Tyagi V, Image forgery detection: Survey and future directions, in Data Engineering and Applications, edited by R K Shukla, J Agrawal, S Sharma, G Singh Tomer (Springer, Singapore) 2019, 163–194, https://doi.org/ 10.1007/978-981-13-6351-1_14.

  • Bilal M, Habib H A, Mehmood Z, Saba T & Rashid M, Single and multiple copy–move forgery detection and localization in digital images based on the sparsely encoded distinctive features and DBSCAN clustering, Arab J Sci Eng (AJSE), 45(3) (2019) 2975–2992, https://doi.org/10.1007/ s13369-019-04238-2.

  • Xiu B, Pun L, Yuan C-M & Chen X, Multi-scale feature extraction and adaptive matching for copy-move forgery detection, Multimed Tools Appl, 77(1) (2016) 363–385.

  • Agarwal R & Verma O P, An efficient copy move forgery detection using deep learning feature extraction and matching algorithm, Multimed Tools Appl, 79 (2019) 7355–7376.

  • Jain P, Bajpai M & Pamula R, A modified DBSCAN algorithm for anomaly detection in time-series data with seasonality, Int Arab J Inf Technol, 19(2022).

  • Hegazi A, Taha A & Selim M M, An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal, J King Saud Univ - Comput Inf Sci, 33(9) (2021) 1055–1063.

  • Singh K N, Singh J N & Kumar K W, Image classification using SLIC superpixel and FAAGKFCM image segmentation, IET Image Process, 14 (2020) 487–494.

  • Chen J, Li Z & Huang B, Linear spectral clustering superpixel, IEEE Trans Image Process, 26(7) (2017) 3317–3330.

  • Liu M Y, Tuzel O, Ramalingam S & Chellappa R, Entropy rate superpixel segmentation, in Proc IEEE Con Comput Vis Pattern Recognit, 2011, 2097–2104

  • Bergh V D, Boix M, Roig X G, SEEDS: superpixels extracted via energy-driven sampling, Int J Comput Vis, 111 (2015) 298–314.

  • Xing Y, Zhong L & Zhong X, Study of clustering algorithm in object tracking and image segmentation, Wirel Commun Mob Comput (2022) 1–15. https://doi.org/10.1155/ 2022/7205929.

  • Levinshtein A, Stere A, Kutulakos K N, Fleet D J, Dickinson S J & Siddiqi K, TurboPixels: fast superpixels using geometric flows, IEEE Trans Pattern Anal Mach Intell, 31(12) (2009) 2290–2297.

  • Shen J, Hao X, Liang Z, Liu Y, Wang W & Shao L, Real-Time superpixel segmentation by DBSCAN clustering algorithm, IEEE Trans on Image Processing, 25(12) (2016) 5933–5942.

  • Martin D, Fowlkes C, Tal D & Malik J, A Database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, Proc IEEE Int Conf Comput Vis, 2 (2001) 416–423, doi: 10.1109/ICCV.2001.937655.

  • Simonyan K & Zisserman A, Very deep convolutional networks for large-scale image recognition, (2014), arXiv 1409.1556.

  • Krizhevsky A, Sutskever I & Hinton G E, Imagenet classification with deep convolutional neural networks, NIPS, (2012), 1106–1114.

  • Ryu S J, Kirchner M, Lee M J & Lee H K, Rotation invariant localization of duplicated image regions based on Zernike moments, IEEE Trans Inf Forensics Secur, 8(8) (2013) 1355–1370.

  • Chou C L & Lee J C, Copy-move forgery detection based on local gabor wavelets patterns, in Int Conf Security Intell Comput Big-Data Services (2017), 47-56.

  • Yang F, Li J, Lu W & Weng J, Copy-move forgery detection based on hybrid features, Eng Appl Artif Intell, 59 (2017) 73–83.

  • Yang B S, Guo X, Xia H Z & Chen X, A copy-move forgery detection method based on CMFD-SIFT, Multimed Tools Appl, 77(1) (2018) 837–855.

  • Al-Qershi O M & Khoo B E, Enhanced block-based copy-move forgery detection using K-means clustering, Multidim Syst Sign Process, (2018), 1–25.


Abstract Views: 129

PDF Views: 85




  • A Novel Approach to Detect Copy Move Forgery Using Deep Learning

Abstract Views: 129  |  PDF Views: 85

Authors

Mamta
Computer Engineering Department, J C Bose University of Science and Technology, YMCA, Faridabad 121 006, Haryana, India
Anuradha Pillai
Computer Engineering Department, J C Bose University of Science and Technology, YMCA, Faridabad 121 006, Haryana, India
Deepika Punj
Computer Engineering Department, J C Bose University of Science and Technology, YMCA, Faridabad 121 006, Haryana, India

Abstract


With the development of readily available image editing tools, manipulating an image has become a universal issue. To check the authenticity, it is necessary to identify how various images might be forged and the way they might be detected using various forgery detection approaches. The importance of detecting copy-move forgery is that it identifies the integrity of an image, which helps in fraud detection at various places such as courtrooms, news reports. This article presents an appropriate technique to detect Copy-Move forgery in which to some extent an image is copied and pasted onto an equivalent image to hide some object or to make duplication. The input image is segmented using the real-time superpixel segmentation algorithm DBSCAN (Density based spatial clustering of application with noise). Due to the high accuracy rate of the VGGNet 16 architecture, it is utilized for feature extraction of segmented images, which will also enhance the efficiency of the overall technique while matching the extracted patches using adaptive patch matching algorithm. The experimental results reveal that the proposed deep learning-based architecture is more accurate in identifying the tempered area even when the images are noisy and can save computational time as compared to existing architectures. For future research, the technique can be enhanced to work on other forgery detection techniques such as image splicing and multi-cloned images.


Keywords


Adaptive patch matching, CNN, Copy move forgery detection, DBSCAN, VGGNet

References