Algoritmo híbrido mediante descriptores Markov y SIFT para la detección de la falsificación de imágenes
DOI:
https://doi.org/10.17533/udea.redin.20211165Palabras clave:
Copiar-Mover, Markov, Remuestreo, SIFT, EmpalmeResumen
Hoy en día, la falsificación de imágenes es común debido a la masificación de las cámaras digitales de alta resolución y bajo costo, junto con la accesibilidad de los programas de computadora para el procesamiento de imágenes. Todos los medios de comunicación se ven afectados por este tema, lo que hace que el público dude de la noticia. Aunque la modificación de imágenes es un proceso común en el entretenimiento, cuando las imágenes se toman como evidencia en un proceso legal, la alteración no puede considerarse trivial. La ciencia forense digital tiene el desafío de garantizar la precisión y la integridad de las imágenes digitales para superar este problema. Esta investigación introduce un algoritmo para detectar los principales tipos de alteraciones basadas en píxeles, como copy-move, resamplig y splicing en imágenes digitales. Para la evaluación del algoritmo se utilizaron las bases de datos CVLAB, CASIA V1, Columbia y Uncompressed Columbia. Se evaluaron 7.100 imágenes, de las cuales 3666 eran auténticas, 791 tenían resampling, 2213 tenían splicing y 430 tenían falsificaciones de copy-move. El algoritmo detectó todas las alteraciones basadas en pixeles con una precisión del 91%. Las principales novedades de la propuesta son el reducido número de características necesarias para la identificación y su robustez al formato y tamaño de la imagen.
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H. Farid, “Image forgery detection,” IEEE Signal processing magazine, vol. 26, no. 2, Mar. 2009. [Online]. Available: https://bit.ly/3vrkjuI
M. A. Qureshi and M. Deriche, “A bibliography of pixel-based blind image forgery detection techniques,” Signal Processing: Image Communication, vol. 39, no. Parte A, Nov. 2015.[Online]. Available: https://doi.org/10.1016/j.image.2015.08.008
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. [Online]. Available: https://doi.org/10.1016/j.jnca.2016.09.008
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. IEEE, 2016, pp. 694–704.
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. [Online]. Available: https://doi.org/10.1016/j.engappai.2016.12.022
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). IEEE, 2013, pp. 188–193.
H. Bay, T. Tuytelaars, and L. V. Gool, “SURF: Speeded Up Robust Features,” in European Conference on Computer Vision. Belín, Alemania: Springer, 2006, pp. 404–417.
H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, “Speeded-Up Robust Features (SURF),” Computer Vision and Image Understanding, vol. 110, no. 3, Jun. 2008. [Online]. Available: https://doi.org/10.1016/j.cviu.2007.09.014
B. Shivakumar and S. S. Baboo, “Detection of Region Duplication Forgery in Digital Images Using SURF,” International Journal of Computer Science Issues, vol. 8, no. 4, Jul. 2011. [Online]. Available: https://bit.ly/3p6cmKh
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, no. 51, Apr. 22, 2021. [Online]. Available: https://doi.org/10.22430/22565337.1671
L. Mera-Jiménez and J. F. Ochoa-Gómez, “Convolutional Neural Network for the Classification of Independent Components of rs-fMRI,” TecnoLógicas, vol. 24, no. 50, Mar. 1, 2021. [Online]. Available: https://doi.org/10.22430/22565337.1626
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). IEEE, 2019, pp. 1–6.
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, no. 3, Mar. 20, 2021. [Online]. Available: https://doi.org/10.3390/jimaging7030059
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. [Online]. Available: https://doi.org/10.1007/s11063-021-10620-9
N. Goel, S. Kaur, and R. Bala, “Dual branch convolutional neural network for copy move forgery detection,” IET Image Processing, vol. 15, no. 3, Dec. 24, 2020. [Online]. Available: https://doi.org/10.1049/ipr2.12051
S.-P. Li, “Resampling forgery detection in JPEG-compressed images,” in 2010 3rd International Congress on Image and Signal Processing. IEEE, 2010, pp. 1166–1170.
A. C. Popescu and H. Farid, “Exposing Digital Forgeries by Detecting Traces of Re-sampling,” IEEE Transactions on Signal Processing, vol. 53, no. 2, Jul. 15, 2005. [Online]. Available: https://bit.ly/3vjKmng
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. IEEE, 2007, pp. 439–446.
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, 2007, pp. 51–62.
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. New York, NY: Springer, 2012, pp. 181–189.
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). Palladam, India: IEEE, 2017, pp. 697–702.
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). Delhi, India: IEEE, 2017, pp. 1–7.
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. Austin, TX, USA: IEEE, 2014, pp. 253–256.
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). Kuwait: IEEE, 2018, pp. 1–6.
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). Tehran, Iran: IEEE, 2015, pp. 1074–1077.
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. Singapore: Springer, 2016, pp. 318–327.
A. Alahmadi and et al., “Passive detection of image forgery using dct and local binary pattern,” Signal, Image and Video Processing, vol. 11, no. 1, Apr. 28, 2016. [Online]. Available: http://www.techweb.com/se/index.html
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. [Online]. Available: https://bit.ly/3b4atFz
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, no. 12, Dec. 2010. [Online]. Available: https://doi.org/10.1016/j.patcog.2012.05.014
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. [Online]. Available: https://doi.org/10.1007/s10044-014-0396-4
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. [Online]. Available: https://doi.org/10.1016/j.neucom.2016.04.068
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, no. 10, Apr. 2018. [Online]. Available: https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-ipr.2017.1131
Y. Rao, J. Ni, and H. Zhao, “Deep Learning Local Descriptor for Image Splicing Detection and Localization,” IEEE Access, vol. 8, Jan. 31, 2020. [Online]. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8977568
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. Singapore: Springer, 2019, pp. 17–27.
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, no. 6, Jul. 2013. [Online]. Available: https://doi.org/10.1016/j.image.2013.03.006
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, 2010, pp. 1469–1472.
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, no. 3, Sep. 2011. [Online]. Available: https://doi.org/10.1109/TIFS.2011.2129512
R. Toldo and A. Fusiello, “Robust Multiple Structures Estimation with J-Linkage,” in European conference on computer vision. Berlin, Heidelberg: Springer, 2008, pp. 537–547.
J. Cortes-Osorio, C. Lopez-Robayo, and N. Hernandez-Betancourt. (2020, Jan. 13,) Computer vision and machine learning lab. Accessed Jan. 21, 2020. [Online]. Available: https://academia.utp.edu.co/jacoper/forgery/
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. Beijing, China: IEEE, 2013, pp. 422–426.
T.-T. Ng, S.-F. Chang, and Q. Sun, “A data set of authentic and spliced image blocks,” Columbia University, New York, Tech. Rep. ADVENT Technical Report 203-2004-3, 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. Toronto,Canada: IEEE, 2006, pp. 549–552.
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, no. 10, Jun. 15, 2015. [Online]. Available: https://doi.org/10.1109/TIFS.2015.2445742
B. Wen and et al., “COVERAGE — A novel database for copy-move forgery detection,” in 2016 IEEE International Conference on Image Processing (ICIP). Phoenix, USA: IEEE, 2016, pp. 161–165.
D. Cozzolino, G. Poggi, and L. Verdoliva, “Copy-move forgery detection based on PatchMatch,” in 2014 IEEE International Conference on Image Processing (ICIP). Paris, France: IEEE, 2014, pp. 5312–5316.
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, no. 20, Mar. 24, 2018. [Online]. Available: https://doi.org/10.1007/s11042-018-5899-3
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. [Online]. Available: https://doi.org/10.1016/j.ijleo.2018.07.021
N. Hema-Rajini, “Image Forgery Identification using Convolution Neural Network,” International Journal of Recent Technology and Engineering, vol. 8, no. Special Issue 4, Jun. 2019. [Online]. Available: https://www.ijrte.org/wp-content/uploads/papers/v8i1s4/A10550681S419.pdf
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.
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