Published November 2, 2021 | Version v1
Journal article Open

Hybrid algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors

Creators

  • 1. Universidad Tecnológica de Pereira

Contributors

  • 1. Universidad de Antioquia

Description

Today, image forgery is common due to the massification of low-cost/high-resolution digital cameras, along with the accessibility of computer programs for image processing. All media is affected by this issue, which makes the public doubt the news. Though image modification is a typical process in entertainment, when images are taken as evidence in a legal process, modification cannot be considered trivial. Digital forensics has the challenge of ensuring the accuracy and integrity of digital images to overcome this issue. This investigation introduces an algorithm to detect the main types of pixel-based alterations such as copy-move forgery, resampling, and splicing in digital images. For the evaluation of the algorithm, CVLAB, CASIA V1, Columbia, and Columbia Uncompressed datasets were used. Of 7100 images evaluated, 3666 were unaltered, 791 had resampling, 2213 had splicing, and 430 had copy-move forgeries. The algorithm detected all proposed forgery pixel methods with an accuracy of 91%. The main novelties of the proposal are the reduced number of features needed for identification and its robustness for the file format and image size.

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References

  • H. Farid, "Image forgery detection," IEEE Signal processing magazine, vol. 26, Mar. 2009.
  • M. A. Qureshi and M. Deriche, "A bibliography of pixel-based blind image forgery detection techniques," Signal Processing: Image Communication, vol. 39, Nov. 2015.
  • H. Farid, Photo forensics. MIT Press, 2016.
  • N. B. A. Warif and et al., "Copy-move forgery detection: Survey, challenges and future directions," Journal of Network and Computer Applications, vol. 75, Nov. 2016.
  • S. S. Mangat and H. Kaur, "Improved copy-move forgery detection in image by feature extraction with KPCA and adaptive method," in 2016 2nd International Conference on Next Generation Computing Technologies (NGCT)Computers and You, pp. 694–704, IEEE, 2016.
  • F. Yang, J. Li, W. Lu, and J. Weng, "Copy-move forgery detection based on hybrid features," Engineering Applications of Artificial Intelligence, vol. 59, Mar. 2017.
  • M. F. Hashmi, A. R. Hambarde, and A. G. Keskar, "Copy Move Forgery Detection using DWT and SIFT Features ," in 20J 3 J 3th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 188–193, IEEE, 2013.
  • H. Bay, T. Tuytelaars, and L. V. Gool, "SURF: Speeded Up Robust Features," in European Conference on Computer Vision, pp. 404–417, Belín, Alemania: Springer, 2006.
  • H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, "Speeded-Up Robust Features (SURF)," Computer Vision and Image Understanding, vol. 110, Jun. 2008.
  • B. Shivakumar and S. S. Baboo, "Detection of Region Duplication Forgery in Digital Images Using SURF," International Journal of Computer Science Issues, vol. 8, Jul. 2011.
  • L. W. Hernández-González, D. A. Curra-Sosa, R. Pérez-Rodríguez, and P. D. Zambrano-Robledo, "Modeling Cutting Forces in High-Speed Turning using Artificial Neural Networks," TecnoLogicas, vol. 24, Apr. 22, 2021.
  • L. Mera-Jiménez and J. F. Ochoa-Gómez, "Convolutional Neural Network for the Classification of Independent Components of rs-fMRI," TecnoLógicas, vol. 24, Mar. 1, 2021.
  • A. B. Z. Abidin, H. B. A. Majid, A. B. A. Samah, and H. B. Hashim, "Copy-move image forgery detection using deep learning methods: a review," in 2019 6th International Conference on Research and Innovation in Information Systems (ICRIIS), pp. 1–6, IEEE, 2019.
  • Y. Rodriguez-Ortega, D. Ballesteros, and D. Renza, "Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics," Journal of Imaging, vol. 7, Mar. 20, 2021.
  • A. Jaiswal and R. Srivastava, "Detection of copy-move forgery in digital image using multi-scale, multi-stage deep learning model," Neural Processing Letters, Aug. 12, 2021.
  • N. Goel, S. Kaur, and R. Bala, "Dual branch convolutional neural network for copy move forgery detection," IET Image Processing, vol. 15, Dec. 24, 2020.
  • S.-P. Li, "Resampling forgery detection in JPEG-compressed images," in 2010 3rd International Congress on Image and Signal Processing, pp. 1166–1170, IEEE, 2010.
  • A. C. Popescu and H. Farid, "Exposing Digital Forgeries by Detecting Traces of Re-sampling," IEEE Transactions on Signal Processing, vol. 53, Jul. 15, 2005.
  • B. Mahdian and S. Saic, "On periodic properties of interpolation and their application to image authentication," in Third International Symposium on Information Assurance and Security, pp. 439–446, IEEE, 2007.
  • Y. Q. Shi, C. Chen, and W. Chen, "A natural image model approach to splicing detection," in Proceedings of the 9th workshop on Multimedia & security, (Dallas, Texas), pp. 51–62, 2007.
  • Y. Zhang, C. Zhao, Y. Pi, , and S. Li, "Revealing image splicing forgery using local binary patterns of dct coefficients," in Communications, Signal Processing, and Systems (Q. Liang and et al., eds.), pp. 181–189, New York, NY: Springer, 2012.
  • M. F. Jwaid and T. N. Baraskar, "Study and analysis of copy-move & splicing image forgery detection techniques," in 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 697–702, Palladam, India: IEEE, 2017.
  • N. K. Gill, R. Garg, and E. A. Doegar, "A review paper on digital image forgery detection techniques," in 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–7, Delhi, India: IEEE, 2017.
  • A. A. Alahmadi, M. Hussain, H. Aboalsamh, G. Muhammad, and G. Bebis, "Splicing image forgery detection based on dct and local binary pattern," in 2013 IEEE Global Conference on Signal and Information Processing, pp. 253–256, Austin, TX, USA: IEEE, 2014.
  • A. Shah and E. El-Alfy, "Image Splicing Forgery Detection Using DCT Coefficients with Multi-Scale LBP," in 2018 International Conference on Computing Sciences and Engineering (ICCSE), pp. 1–6, Kuwait: IEEE, 2018.
  • F. Hakimi, M. Hariri, and F. GharehBaghi, "Image splicing forgery detection using local binary pattern and discrete wavelet transform," in 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), pp. 1074–1077, Tehran, Iran: IEEE, 2015.
  • M. Kaur and S. Gupta, "A passive blind approach for image splicing detection based on dwt and lbp histograms," in International Symposium on Security in Computing and Communication, pp. 318–327, Singapore: Springer, 2016.
  • A. Alahmadi and et al., "Passive detection of image forgery using dct and local binary pattern," Signal, Image and Video Processing, vol. 11, Apr. 28, 2016.
  • F. Hakimi, I. Zanjan, and I. Hariri, "Image-Splicing Forgery Detection Based On Improved LBP and K-Nearest Neighbors Algorithm," Electronics Information & Planning, vol. 3, Jul. 15, 2015.
  • Z. He, W. Lu, W. Sun, and J. Huang, "Digital image splicing detection based on Markov features in DCT and DWT domain," Pattern Recognition, vol. 45, Dec. 2010.
  • E.-S. M. El-Alfy and M. A. Qureshi, "Combining spatial and DCT based Markov features for enhanced blind detection of image splicing," Pattern Analysis and Applications, vol. 18, Aug. 2015.
  • C. Li, Q. Ma, L. Xiao, M. Li, and A. Zhang, "Image splicing detection based on Markov features in QDCT domain," Neurocomputing, vol. 228, Mar. 8, 2017.
  • H. Sheng, X. Shen, Y. Lyu, Z. Shi, and S. Ma, "Image splicing detection based on markov features in discrete octonion cosine transform domain," IET Image Processing, vol. 12, Apr. 2018.
  • Y. Rao, J. Ni, and H. Zhao, "Deep Learning Local Descriptor for Image Splicing Detection and Localization," IEEE Access, vol. 8, Jan. 31, 2020.
  • A. Kumar, C. S. Prakash, S. Maheshkar, and V. Maheshkar, "Markov Feature Extraction Using Enhanced Threshold Method for Image Splicing Forgery Detection," in Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, pp. 17–27, Singapore: Springer, 2019.
  • I. Amerini and et al., "Copy-move forgery detection and localization by means of robust clustering with J-Linkage," Signal Processing: Image Communication, vol. 28, Jul. 2013.
  • A. Vedaldi and B. Fulkerson, "Vlfeat: an open and portable library of computer vision algorithms," in Proceedings of the 18th ACM international conference on Multimedia, pp. 1469–1472, 2010.
  • I. Amerini, L. Ballan, R. Caldelli, A. D. Bimbo, and G. Serra, "A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery," IEEE Transactions on Information Forensics and Security, vol. 6, Sep. 2011.
  • R. Toldo and A. Fusiello, "Robust Multiple Structures Estimation with J-Linkage," in European conference on computer vision, pp. 537–547, Berlin, Heidelberg: Springer, 2008.
  • J. Cortes-Osorio, C. Lopez-Robayo, and N. Hernandez-Betancourt, "Computer vision and machine learning lab," Jan. 13, 2020. Accessed Jan. 21, 2020.
  • J. Dong, W. Wang, and T. Tan, "CASIA Image Tampering Detection Evaluation Database," in 2013 IEEE China Summit and International Conference on Signal and Information Processing, pp. 422–426, Beijing, China: IEEE, 2013.
  • T.-T. Ng, S.-F. Chang, and Q. Sun, "A data set of authentic and spliced image blocks," Tech. Rep. ADVENT Technical Report 203-2004-3, Columbia University, New York, Jun. 2004.
  • Y.-F. Hsu and S.-F. Chang, "Detecting image splicing using geometry invariants and camera characteristics consistency," in 2006 IEEE International Conference on Multimedia and Expo, pp. 549–552, Toronto,Canada: IEEE, 2006.
  • E. Ardizzone, A. Bruno, and G. Mazzola, "Copy–move forgery detection by matching triangles of keypoints," IEEE Transactions on Information Forensics and Security, vol. 10, Jun. 15, 2015.
  • B. Wen and et al., "COVERAGE — A novel database for copy-move forgery detection," in 2016 IEEE International Conference on Image Processing (ICIP), pp. 161–165, Phoenix, USA: IEEE, 2016.
  • D. Cozzolino, G. Poggi, and L. Verdoliva, "Copy-move forgery detection based on PatchMatch," in 2014 IEEE International Conference on Image Processing (ICIP), pp. 5312–5316, Paris, France: IEEE, 2014.
  • C. S. Prakash, A. Kumar, S. Maheshkar, and V. Maheshkar, "An integrated method of copy-move and splicing for image forgery detection," Multimedia Tools and Applications, vol. 77, Mar. 24, 2018.
  • S. Sharma and U. Ghanekar, "A hybrid technique to discriminate Natural Images, Computer Generated Graphics Images, Spliced, Copy Move tampered images and Authentic images by using features and ELM classifier," Optik, vol. 172, Nov. 2018.
  • N. Hema-Rajinifi, "Image Forgery Identification using Convolution Neural Network," International Journal of Recent Technology and Engineering, vol. 8, Jun. 2019.
  • C. Lopez, "Algoritmo híbrido para la identificación de falsificaciones de tipo de copy-move, splicing y resampling en imágenes digitales usando markov y sift," m.s. thesis, Universidad Tecnológica de Pereira, Pereira, Colombia, 2019.