access icon free Hybrid deep learning and machine learning approach for passive image forensic

Image forgery detection using traditional algorithms takes much time to find forgeries. The new emerging methods for the detection of image forgery use a deep neural network algorithm. A hybrid deep learning (DL) and machine learning-based approach is used in this study for passive image forgery detection. A DL algorithm classifies images into the forged and not forged categories, whereas colour illumination localises forgery. The simulated results are compared to other algorithms on public datasets. The simulated results achieved 99% accuracy for CASIA1.0, 98% accuracy for CASIA2.0, 98% accuracy for BSDS300, 97% accuracy for DVMM, and 99% accuracy for CMFD image manipulation dataset.

Inspec keywords: Gabor filters; learning (artificial intelligence); image segmentation; iris recognition; image forensics; image coding; feature extraction; image classification; neural nets

Other keywords: CMFD image manipulation dataset; forged forged categories; passive image forensic; machine learning approach; forgeries; not forged categories; machine learning-based approach; DL algorithm; deep neural network algorithm; passive image forgery detection; emerging methods

Subjects: Image recognition; Computer vision and image processing techniques; Knowledge engineering techniques; Optical, image and video signal processing; Neural computing techniques; Image and video coding

References

    1. 1)
      • 4. Dong, J., Wang, W.: ‘CASIA tampered image detection evaluation (TIDE) database, v1.0 and v2.0’, http://forensics.idealtest.org/, 2011.
    2. 2)
      • 22. Gao, B.B., Xing, C., Xie, C.W., et al: ‘Deep label distribution learning with label ambiguity’, IEEE Trans. Image Process., 2017, 26, (6), pp. 28252838.
    3. 3)
      • 12. Amerini, I., Ballan, L., Bimbo, A.D.: ‘A SIFT-based forensic method for copy–move attack detection and transformation recovery’, IEEE Trans. Inf. Forensics Sec., 2011, 6, (3), pp. 10991110.
    4. 4)
      • 15. Wang, W., Shen, J.: ‘Video salient object detection via fully convolutional networks’, IEEE Trans. Image Process., 2018, 27, (1), pp. 3849.
    5. 5)
      • 27. Saha, M., Chakraborty, C.: ‘Her2net: A deep framework for semantic segmentation and classification of cell membranes and nuclei in breast cancer evaluation’, IEEE Trans. Image Process., 2018, 27, (5), pp. 21892200.
    6. 6)
      • 23. Rao, Y., Ni, J.: ‘A deep learning approach to detection of splicing and copy-move forgeries in images’. Proc. of the IEEE Int. Workshop on Information Forensics and Security (WIFS), Abu Dhabi, UAE, 2016, pp. 16.
    7. 7)
      • 21. Chen, C., McCloskey, S.: ‘Image splicing detection via camera response function analysis’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 2017, pp. 18761885.
    8. 8)
      • 19. Zhan, M.G.T., Zhang, P., Miao, Q.: ‘Superpixel-based difference representation learning for change detection in multispectral remote sensing images’, IEEE Trans. Geosci. Remote Sens., 2017, 55, (5), pp. 26582672.
    9. 9)
      • 32. Thakur, A., Jindal, N.: ‘Machine learning based saliency algorithm for image forgery classification and localization’. Int. Conf. on Secure Cyber Computing and Communication (ICSCCC), Jalandhar, India, India, 2018, pp. 451456.
    10. 10)
      • 35. Muhammad, G., Al–Hammadi, M., Hussain, M., et al: ‘Image forgery detection using steerable pyramid transform and local binary pattern’. Machine Vision and Applications, 2014, pp. 985995.
    11. 11)
      • 11. Husain, F., Schulz, H., Dellen, B., et al: ‘Combining semantic and geometric features for object class segmentation of indoor scenes’, IEEE Robot. Autom. Lett., 2017, 2, (1), pp. 4955.
    12. 12)
      • 14. Nithiya, R., Veluchamy, S.: ‘Key point descriptor based copy and move image forgery detection system’. Proc. of the Second Int. Conf. on Science Technology Engineering and Management (ICONSTEM), Chennai, India, 2016, pp. 577581.
    13. 13)
      • 18. Girshick, R., Donahue, J., Darrell, T.: ‘Region-based convolutional networks for accurate object detection and segmentation’, IEEE Trans. Pattern Anal. Mach. Intell., 2016, 38, (1), pp. 142156.
    14. 14)
      • 8. Thirunavukkarasu, V., Kumar, J.S.: ‘Passive image tamper detection based on fast retina keypoint descriptor’. Proc. of the IEEE Int. Conf. on Advances in Computer Applications (ICACA), Coimbatore, India, 2016, pp. 279285.
    15. 15)
      • 29. Tralic, D., Zupancic, I., Grgic, S., et al: ‘Comofod – new database for copy-move forgery detection’. Int. Symp. ELMAR, Zadar, Croatia, 2013, pp. 4954.
    16. 16)
      • 9. Cheng, D., Meng, G.: ‘Fusion net edge aware deep convolutional networks for semantic segmentation of remote sensing harbor images’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2017, 10, (12), pp. 57695783.
    17. 17)
      • 20. Keglevic, M., Sablatnig, R.: ‘Learning a similarity measure for striated tool marks using convolutional neural networks’. Computer Vision Lab - TU Wien, Favoritenstr. 9/183–2, A-1040 Vienna, Austria, 2016, pp. 16.
    18. 18)
      • 17. Chen, J., Kang, X., Liu, Y., et al: ‘Median filtering forensics based on convolutional neural networks’, IEEE Signal Process. Lett., 2015, 22, (11), pp. 18491853.
    19. 19)
      • 30. Thakur, A., Jindal, N.: ‘Geometrical attack classification using DCNN and forgery localization using machine learning’, Int. J. Recent Technol. Eng. (IJRTE), 2019, 7, (5S2), pp. 22773878.
    20. 20)
      • 1. Caliskan, A., Cevik, U.: ‘An efficient noisy pixels detection model for CT images using extreme learning machines’, Teh. Vjesn., 2018, 25, (3), pp. 679686.
    21. 21)
      • 31. Thakur, A., Jindal, N.: ‘Image forensics using color illumination, block and key point based approach’, Multimedia Tools Appl., 2018, 77, (19), pp. 2603326053.
    22. 22)
      • 36. V De Weijer, J., Gevers, T, Gijsenij, A.: ‘Edge-based color constancy’, IEEE Trans. Image Process., 2007, 16, (9), pp. 22072214.
    23. 23)
      • 24. Hsu, W.W., Zhang, M., Chen, C.H., et al: ‘The use of deep learning and mean shift to learn global and local processing in human visual perception’. Proc. of the IEEE Int. Conf. on Systems, Man, and Cybernetics, Budapest, Hungary, 2016, pp. 21392144.
    24. 24)
      • 33. Zhao, X., Wang, S., Li, S., et al: ‘Passive image-splicing detection by a 2-D non causal Markov model’, IEEE Trans. Circuits Syst. Video Technol., 2015, 25, (2), pp. 185199.
    25. 25)
      • 26. Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., et al: ‘Convolutional neural networks for medical image analysis: full training or fine tuning?’, IEEE Trans. Med. Imaging, 2016, 35, (5), pp. 12991312.
    26. 26)
      • 6. Tarman, S.H.: ‘M-SIFT: A detection algorithm for copy move image forgery’. Proc. of the 4th IEEE int. Conf. on Signal Processing, Computing, and Control. ISPCC2k17, Solan, India, 2017, pp. 425430.
    27. 27)
      • 2. Zhao, W., Du, S., William, J.: ‘Object-based convolutional neural network for high-resolution imagery classification’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2017, 10, (7), pp. 33863396.
    28. 28)
      • 3. Jian, L., Xiaolong, L., Bin, Y., et al: ‘Segmentation-based image copy-move forgery detection scheme’, IEEE Trans. Inf. Forensics Sec., 2015, 10, (3), pp. 507518.
    29. 29)
      • 16. Pagnutti, G., Minto, L., Zanuttigh, P.: ‘Segmentation and semantic labeling of RGBD data with convolutional neural networks and surface fitting’, IET Comput. Vis., 2017, 11, (8), pp. 633642.
    30. 30)
      • 13. Li, J.: ‘Active learning for hyperspectral image classification with a stacked autoencoders based neural network’. Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, Japan, 2015, pp. 14.
    31. 31)
      • 28. Ouyang, J., Liu, Y., Liao, M: ‘Copy-move forgery detection based on deep learning’. Int. Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, People's Republic of China, 2017, pp. 15.
    32. 32)
      • 5. Bunk, J., Bappy, J.H., Mohammed, T.M., et al: ‘Detection and localization of image forgeries using resampling features and deep learning’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA, 2017, pp. 18811889.
    33. 33)
      • 25. Zuo, H., Fan, H., Blasch, E., et al: ‘Combining convolutional and recurrent neural networks for human skin detection’, IEEE Signal Process. Lett., 2017, 24, (3), pp. 289293.
    34. 34)
      • 34. He, Z., Lu, W., Sun, W., et al: ‘Digital image splicing detection based on Markov features in DCT and DWT domain’, Pattern Recognit., 2012, 45, (12), pp. 42924299.
    35. 35)
      • 10. Li, Y., Liu, X.: ‘SIFT keypoint removal and injection via convex relaxation’, IEEE Trans. Inf. Forensics Sec., 2016, 11, (8), pp. 17221735.
    36. 36)
      • 7. Fengli, Z, Qinghua, L.: ‘Deep learning-based data forgery detection in automatic generation control’. Proc. of the IEEE Conf. on Communications and Network Security (CNS): Int. Workshop on Cyber-Physical Systems Security (CPS-Sec), Las Vegas, NV, USA, 2017, pp. 400404.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2019.1291
Loading

Related content

content/journals/10.1049/iet-ipr.2019.1291
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading