International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
권호 표지
DOI QR코드

DOI QR Code

A Hybrid Learning Model to Detect Morphed Images

  • 투고 : 2022.06.05
  • 발행 : 2022.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

키워드

참고문헌

  1. Dua, Shilpa, Jyotsna Singh, and Harish Parthasarathy. "Image forgery detection based on statistical features of block DCT coefficients." Procedia Computer Science 171 (2020): 369-378. https://doi.org/10.1016/j.procs.2020.04.038
  2. Meena, Kunj Bihari, and Vipin Tyagi. "A hybrid copy-move image forgery detection technique based on Fourier-Mellin and scale invariant feature transforms." Multimedia Tools and Applications 79.11 (2020): 8197-8212. https://doi.org/10.1007/s11042-019-08343-0
  3. Al_Azrak, Faten Maher, et al. "An efficient method for image forgery detection based on trigonometric transforms and deep learning." Multimedia Tools and Applications 79.25 (2020): 18221-18243. https://doi.org/10.1007/s11042-019-08162-3
  4. Z Zhang, Y Zhou, J Kang and Y Ren, Study of image splicing detection., International Conference on Intelligent Computing. Springer, Berlin, Heidelberg, 2008
  5. Marra, Francesco, et al. "A full-image full-resolution end-to-end-trainable CNN framework for image forgery detection." IEEE Access 8 (2020): 133488-133502. https://doi.org/10.1109/access.2020.3009877
  6. Geetha, M., et al. "A novel approach for image forgery detection using improved crow search algorithm." Materials Today: Proceedings (2021).
  7. Rao, Yuan, Jiangqun Ni, and Hao Xie. "Multi-semantic CRF-based attention model for image forgery detection and localization." Signal Processing 183 (2021): 108051. https://doi.org/10.1016/j.sigpro.2021.108051
  8. El Biach, Fatima Zahra, et al. "Encoder-decoder based convolutional neural networks for image forgery detection." Multimedia Tools and Applications (2021): 1-18.
  9. A Thakur and N Jindal, Hybrid deep learning and machine learning approach for passive image forensic, IET Image Processing 14.10 (2020): 1952-1959 https://doi.org/10.1049/iet-ipr.2019.1291
  10. Albawi, Saad, Tareq Abed Mohammed, and Saad Al-Zawi., Understanding of a convolutional neural network, 2017 International Conference on Engineering and Technology (ICET). Ieee, 2017
  11. Kadam, Kalyani, Swati Ahirrao, and Ketan Kotecha. "AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI." International Journal of Electrical & Computer Engineering (2088-8708) 11.5 (2021).
  12. Jaiswal, A Kumar, and R Srivastava, A technique for image splicing detection using Hybrid feature set, Multimedia Tools and Applications 79.17 (2020): 11837-11860 https://doi.org/10.1007/s11042-019-08480-6
  13. Venkatesh, Sushma, et al. "Face morphing attack generation & detection: A comprehensive survey." IEEE Transactions on Technology and Society (2021).
  14. Reyna, Roberto A., et al. "Implementation of the SVM neural network generalization function for image processing." Proceedings Fifth IEEE International Workshop on Computer Architectures for Machine Perception. IEEE, 2000.
  15. R Raghavendra, K B Raja, S Marcel and C Busch, Face presentation attack detection across spectrum using time-frequency descriptors of maximal response in laplacian scale-space, 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2016
  16. Makrushin, Andrey, Tom Neubert, and Jana Dittmann., Automatic generation and detection of visually faultless facial morphs, International Conference on Computer Vision Theory and Applications. Vol. 7. SCITEPRESS, 2017
  17. M Hildebrandt, T Neubert, A Makrushin and J Dittmann , Benchmarking face morphing forgery detection: Application of stirtrace for impact simulation of different processing steps, 2017 5th International Workshop on Biometrics and Forensics (IWBF). IEEE, 2017
  18. Neubert, Tom, Face morphing detection: An approach based on image degradation analysis, International Workshop on Digital Watermarking. Springer, Cham, 2017
  19. J Bunk, J H Bappy, T M Mohammed, L Nataraj, A Flenner, B S Manjunath, S Chandrasekaran, A Chowdhury and L Peterson, Detection and localization of image forgeries using resampling features and deep learning, 2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW). IEEE, 2017
  20. Saini, Hardeep, M-SIFT: A detection algorithm for copy move image forgery, 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC). IEEE, 2017
  21. Zhang, Fengli, and Qinghua Li., Deep learning-based data forgery detection in automatic generation control, 2017 IEEE Conference on Communications and Network Security (CNS). IEEE, 2017
  22. V Thirunavukkarasu, and J S Kumar, Passive image tamper detection based on fast retina key point descriptor, 2016 IEEE International Conference on Advances in Computer Applications (ICACA). IEEE, 2016.
  23. D Cheng, G Meng, S Xiang and C Pan, FusionNet: Edge aware deep convolutional networks for semantic segmentation of remote sensing harbor images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10.12 (2017): 5769-5783 https://doi.org/10.1109/JSTARS.2017.2747599
  24. Y Li, J Zhou, A Cheng, X Liu and Y Y Tang, SIFT keypoint removal and injection via convex relaxation, IEEE Transactions on Information Forensics and Security 11.8 (2016): 1722-1735 https://doi.org/10.1109/TIFS.2016.2553645
  25. I Amerini, L Ballan, R Caldelli, A D Bimbo, G Serra, A sift-based forensic method for copy-move attack detection and transformation recovery, IEEE transactions on information forensics and security 6.3 (2011): 1099-1110 https://doi.org/10.1109/TIFS.2011.2129512
  26. F Husain, H Schulz, B Dellen, C Torras and S Behnke, Combining semantic and geometric features for object class segmentation of indoor scenes, IEEE Robotics and Automation Letters 2.1 (2016): 49-55 https://doi.org/10.1109/LRA.2016.2532927
  27. Li, Jiming, Active learning for hyperspectral image classification with a stacked autoencoders based neural network, 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2015
  28. Zhao, Wenzhi, Shihong Du, and William J. Emery, Object-based convolutional neural network for high-resolution imagery classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10.7 (2017): 3386-3396 https://doi.org/10.1109/JSTARS.2017.2680324
  29. Wang, Wenguan, Jianbing Shen, and Ling Shao, Video salient object detection via fully convolutional networks, IEEE Transactions on Image Processing 27.1 (2017): 38-49 https://doi.org/10.1109/TIP.2017.2754941
  30. Jassim, Sabah, and Aras Asaad, Automatic detection of image morphing by topology-based analysis, 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018
  31. D Mellouli, TM Hamdani, MB Ayed and AM Alimi, Morph-CNN: a morphological convolutional neural network for image classification, International Conference on Neural Information Processing. Springer, Cham, 2017
  32. Raja, Kiran, Sushma Venkatesh, and R. B. Christoph Busch. "Transferable deep-cnn features for detecting digital and print-scanned morphed face images." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2017.
  33. Makrushin, Andrey, Tom Neubert, and Jana Dittmann. "Humans Vs. Algorithms: Assessment of Security Risks Posed by Facial Morphing to Identity Verification at Border Control." VISIGRAPP (4: VISAPP). 2019.
  34. Neubert, Tom, Christian Kraetzer, and Jana Dittmann. "A face morphing detection concept with a frequency and a spatial domain feature space for images on eMRTD." Proceedings of the ACM Workshop on Information Hiding and Multimedia Security. 2019.
  35. Aghdaie, Poorya, et al. "Morph Detection Enhanced by Structured Group Sparsity." Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2022.
  36. Ferrara, Matteo, Annalisa Franco, and Davide Maltoni. "Face morphing detection in the presence of printing/scanning and heterogeneous image sources." IET Biometrics 10.3 (2021): 290-303. https://doi.org/10.1049/bme2.12021
  37. Aghdaie, Poorya, et al. "Attention aware wavelet-based detection of morphed face images." 2021 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2021.
  38. Seibold, Clemens, et al. "Accurate and robust neural networks for face morphing attack detection." Journal of Information Security and Applications 53 (2020): 102526. https://doi.org/10.1016/j.jisa.2020.102526
  39. Venkatesh, Sushma, et al. "Single image face morphing attack detection using ensemble of features." 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020.
  40. Scherhag, Ulrich, et al. "Morphing Attack Detection using Laplace operator based features." Norsk IKT-konferanse for forskning og utdanning. No. 3. 2020.
  41. Scherhag, Ulrich, et al. "Detecting morphed face images using facial landmarks." International Conference on Image and Signal Processing. Springer, Cham, 2018.
  42. https://www.kaggle.com/sophatvathana/casia-dataset
  43. https://www.ee.columbia.edu/ln/dvmm/downloads/AuthSp licedDataSet/AuthSplicedDataSet.htm
  44. Jackins, V., et al. "AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes." The Journal of Supercomputing 77.5 (2021): 5198-5219. https://doi.org/10.1007/s11227-020-03481-x
  45. Brownlee, Jason. "What is the Difference Between a Batch and an Epoch in a Neural Network?." Machine Learning Mastery 20 (2018).
  46. de Santana, Felipe Bachion, Waldomiro Borges Neto, and Ronei J. Poppi. "Random forest as one-class classifier and infrared spectroscopy for food adulteration detection." Food chemistry 293 (2019): 323-332. https://doi.org/10.1016/j.foodchem.2019.04.073
  47. Ham, Jisoo, et al. "Investigation of the random forest framework for classification of hyperspectral data." IEEE Transactions on Geoscience and Remote Sensing 43.3 (2005): 492-501 https://doi.org/10.1109/TGRS.2004.842481
  48. Jaiswal, Ankit Kumar, and Rajeev Srivastava, Image splicing detection using deep residual network, Proceedings of 2nd International Conference on Advanced Computing and Software Engineering (ICACSE). 2019
  49. Z He, W Lu, W Sun and J Huang, Digital image splicing detection based on Markov features in DCT and DWT domain, Pattern recognition 45.12 (2012): 4292-4299 https://doi.org/10.1016/j.patcog.2012.05.014