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Dual adaptive deep convolutional neural network for video forgery detection in 3D lighting environment

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Abstract

Video forgery detection is one of the challenges in this digital era, where the focus is on discovering authenticity. Though there are so many methods available to detect forgeries in the video, there is no method that utilizes illumination-based forgery detection. Hence, this research focuses on establishing the 3D model of the video frame to generate light coefficients in order to detect the forgeries in the video. On the other hand, this paper proposes dual adaptive-Taylor-rider optimization algorithm-based deep convolutional neural network (DA-Taylor-ROA-based DCNN) for video forgery detection, where DCNN is trained using the dual adaptive-Taylor-rider optimization algorithm (DA-TROA) that inherits the adaptive concept and Taylor series within the standard rider optimization algorithm (ROA). For the detection process, the distance-based features from the light coefficients and face objects detected using the Viola–Jones algorithm from the video frames are used. The significance of the method is analyzed using the real images for varying noise conditions based on the performance metrics, such as accuracy, true positive rate, and true negative rate. The percentage improvement of accuracy for proposed DA-Taylor-ROA-based DCNN with respect to Taylor-ROA-Based deep CNN is 4.3626% in the absence of noise, and 1.5985% of accuracy improvement in the presence of speckle noise, respectively.

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References

  1. Tralic, D., Zupancic, I., Grgic, S., Grgic, M.: CoMoFoD—new database for copy-move forgery detection. In: Proceedings ELMAR, pp. 49–54 (2013)

  2. Ghorbani, M., Firouzmand, M., Faraahi, A.: DWT-DCT (QCD) based copy-move image forgery detection. In: 18th International Conference on Systems, Signals and Image Processing, pp. 1–4 (2011)

  3. Su, L., Huang, T., Yang, J.: A video forgery detection algorithm based on compressive sensing. Multimedia Tools Appl. 74(17), 6641–6656 (2015)

    Article  Google Scholar 

  4. Delp, E., Memon, N., Wu, M.: Digital forensics. IEEE Signal Proc. Mag. 26(2), 14–15 (2009)

    Article  Google Scholar 

  5. Saddique, M., Asghar, K., Bajwa, U.I., Hussain, M., Habib, Z.: Spatial video forgery detection and localization using texture analysis of consecutive frames. Adv. Electr. Comput. Eng. 19(3), 97–108 (2019)

    Article  Google Scholar 

  6. Johnson, M.K., Farid, H.: Detecting photographic composites of people. In: Digital Watermarking. Springer, Berlin, pp. 19–33 (2008)

  7. Johnson, M.K., Farid, H.: Exposing digital forgeries through specular highlights on the eye. In: International Workshop on Information Hiding, Springer, Berlin, pp. 311–325 (2007)

  8. Bidokhti, A., Ghaemmaghami, S.: Detection of regional copy/move forgery in MPEG videos using optical flow. In: Artificial Intelligence and Signal Processing (AISP), International Symposium on, pp. 13–17 (2015)

  9. Guo, C., Luo, G., Zhu, Y.: A detection method for facial expression reenacted forgery in videos. In: Tenth International Conference on Digital Image Processing (ICDIP 2018), p. 108061 (2018)

  10. Al-Sanjary, O.I., Ahmed, A.A., Jaharadak, A.A.B., Ali, M.A., Zangana, H.M.: Detection clone an object movement using an optical flow approach. In: IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE), pp. 388–394 (2018)

  11. Su, L., Li, C.: A novel passive forgery detection algorithm for video region duplication. Multidimension. Syst. Signal Process. 29, 1173–1190 (2018)

    Article  MathSciNet  Google Scholar 

  12. Li, L., Wang, X., Zhang, W., Yang, G., Hu, G.: Detecting removed object from video with stationary background. In: The International Workshop on Digital Forensics and Watermarking, pp. 242–252 (2012)

  13. Singh, Gurvinder, Singh, Kulbir: Video frame and region duplication forgery detection based on correlation coefficient and coefficient of variation. Multimedia Tools Appl. 78, 11527–11562 (2019)

    Article  Google Scholar 

  14. Peng, B., Wang, W., Dong, J., Tan, T.: Optimized 3D lighting environment estimation for image forgery detection. IEEE Trans. Inf. Forensics Secur. 12(2), 479–494 (2016)

    Article  Google Scholar 

  15. Kee, E., Farid, H.: Exposing digital forgeries from 3-D lighting environments. In: Proceedings of IEEE International Workshop on Information Forensics and Security, pp. 1–6 (2010)

  16. Peng, B., Wang, W., Dong, J., Tan, T.: Improved 3D lighting environment estimation for image forgery detection. In: Proceedings of IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6 (2015)

  17. Fan, W., Wang, K., Cayre, F., Xiong, Z.: 3D lighting-based image forgery detection using shape-from-shading. In: Proceedings of the 20th European Signal Processing Conference (EUSIPCO), pp. 1777–1781 (2012)

  18. Sitara, K., Mehtre, B.M.: Differentiating synthetic and optical zooming for passive video forgery detection: an anti-forensic perspective. Digit. Investig. 30, 1–11 (2019)

    Article  Google Scholar 

  19. Zhang, Z., Liu, Q.: Detect video forgery by performing transfer learning on deep neural network. In: Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, pp. 415–422 (2020)

  20. Joshi, Vaishali, Jain, Sanjay: Tampering detection and localization in digital video using temporal difference between adjacent frames of actual and reconstructed video clip. Int. J. Inf. Technol. 12, 273–282 (2020)

    Google Scholar 

  21. Meena, K.B., Tyagi, V.: Image forgery detection: survey and future directions. In: Data, Engineering and Applications, pp. 163–194 (2019)

  22. Pomari, T.,, Ruppert, G., Rezende, E., Rocha, A., Carvalho, T.: Image splicing detection through illumination inconsistencies and deep learning. In: Proceedings of 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece (2018)

  23. Rácz A, Bajusz D, Héberger K.: Life beyond the Tanimoto coefficient: similarity measures for interaction fingerprints. J. Cheminform. 10(48) (2018)

  24. Alkawaz, M.H., Sulong, G., Saba, T., Rehman, A.: Detection of copy-move image forgery based on discrete cosine transform. Neural Comput. Appl. 30, 183–192 (2018)

    Article  Google Scholar 

  25. Mangai, S.A., Sankar, B.R., Alagarsamy, K.: Taylor series prediction of time series data with error propagated by artificial neural network. Int. J. Comput. Appl. 89(1), 41–47 (2014)

    Google Scholar 

  26. Binu, D., Kariyappa, B.S.: RideNN: a new rider optimization algorithm-based neural network for fault diagnosis in analog circuits. IEEE Trans. Instrum. Meas. 68(1), 2–26 (2019)

    Article  Google Scholar 

  27. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition, pp. 511–518 (2001)

  28. Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces. In: Siggraph A. (ed) Proceedings of SIGGRAPH 99, pp. 187–194 (1999)

  29. Tu, F., Yin, S., Ouyang, P., Tang, S., Liu, L., Wei, S.: Deep convolutional neural network architecture with reconfigurable computation patterns. IEEE Trans. Very Large Scale Integr. Syst. 25(8), 2220–2233 (2017)

    Article  Google Scholar 

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Authors and Affiliations

  1. Noorul Islam Centre for Higher Education, Kumaracoil, Kanyakumari, Tamilnadu, India

    V. Vinolin

  2. Malla Reddy College of Engineering and Technology, Maisammaguda, Medchal, Telangana, India

    M. Sucharitha

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  1. V. Vinolin
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  2. M. Sucharitha
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Vinolin, V., Sucharitha, M. Dual adaptive deep convolutional neural network for video forgery detection in 3D lighting environment. Vis Comput 37, 2369–2390 (2021). https://doi.org/10.1007/s00371-020-01992-5

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