Abstract
Previous digital image forensics focused on the low-level features that include traces of the image modifying history. In this paper, we present a framework to detect the manipulation of images through a contextual violation. First, we proposed a context learning convolutional neural networks (CL-CNN) that detects the contextual violation in the image. In combination with a well-known object detector such as R-CNN, the proposed method can evaluate the contextual scores according to the combination of objects in the image. Through experiments, we showed that our method effectively detects the contextual violation in the target image.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, M., Fridrich, J., Goljan, M., Lukas, J.: Determining image origin and integrity using sensor noise. IEEE Trans. Inf. Forensics Secur. 3(1), 74–90 (2008)
Choi, C.H., Lee, H.Y., Lee, H.K.: Estimation of color modification in digital images by CFA pattern change. Forensic Sci. Int. 226, 94–105 (2013)
Farid, H.: Exposing digital forgeries from JPEG ghosts. IEEE Trans. Inf. Forensics Secur. 4(1), 154–160 (2009)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Hou, J.U., Jang, H.U., Lee, H.K.: Hue modification estimation using sensor pattern noise. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 5287–5291, October 2014
Hou, J.U., Lee, H.K.: Detection of hue modification using photo response non-uniformity. IEEE Transactions on Circuits and Systems for Video Technology (2016)
Huang, H., Guo, W., Zhang, Y.: Detection of copy-move forgery in digital images using SIFT algorithm. In: Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA 2008, vol. 2, pp. 272–276, December 2008
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
Johnson, M.K., Farid, H.: Exposing digital forgeries in complex lighting environments. IEEE Trans. Inf. Forensics Secur. 2(3), 450–461 (2007)
Kee, E., O’brien, J.F., Farid, H.: Exposing photo manipulation from shading and shadows. ACM Trans. Graph. 33(5), Article No. 165 (2014)
Lin, D., Lu, C., Liao, R., Jia, J.: Learning important spatial pooling regions for scene classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3726–3733 (2014)
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Popescu, A., Farid, H.: Exposing digital forgeries by detecting traces of resampling. IEEE Trans. Sig. Process. 53(2), 758–767 (2005)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Ryu, S.J., Kirchner, M., Lee, M.J., Lee, H.K.: Rotation invariant localization of duplicated image regions based on Zernike moments. IEEE Trans. Inf. Forensics Secur. 8(8), 1355–1370 (2013)
Zhang, F., Du, B., Zhang, L.: Saliency-guided unsupervised feature learning for scene classification. IEEE Trans. Geosci. Remote Sens. 53(4), 2175–2184 (2015)
Acknowledgement
This work was supported by the Institute for Information & communications Technology Promotion (IITP) grant funded by the Korean government (MSIP) (2017-0-01671, Development of high reliability elementary image authentication technology for smart media environment).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Hou, JU., Jang, HU., Park, JS., Lee, HK. (2018). Exposing Digital Forgeries by Detecting a Contextual Violation Using Deep Neural Networks. In: Kang, B., Kim, T. (eds) Information Security Applications. WISA 2017. Lecture Notes in Computer Science(), vol 10763. Springer, Cham. https://doi.org/10.1007/978-3-319-93563-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-93563-8_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93562-1
Online ISBN: 978-3-319-93563-8
eBook Packages: Computer ScienceComputer Science (R0)