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A Novel Method of Cropped Images Forensics in Social Networks

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Pattern Recognition and Computer Vision (PRCV 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13020))

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Abstract

Operation chain forensics has achieved many advanced results, however, the spread of images in Social Networks has brought greater challenges to forensic research. The processing of images by Social Networks reduces the effectiveness of current forensics methods. An example is that if images are exchanged through Social Networks, the features they rely on will no longer be valid for detecting cropped images. To solve this problem, this paper proposes a novel method to complete the task of cropped forensics on Social Networks. By analyzing the image processing characteristics of Social Networks, we designed a special feature named “block artifacts grayscale” (BAGS) for crop detection based on block artifacts generated in JPEG compression. The proposed method is proved more accurate than some previous algorithms in cropped forensics in Social Networks, and it is also robust to serious re-compression. The proposed method modeled on Social Networks can promote other forensics issues. Moreover, the method provides a good direction for determining the process of enhanced filtering in Social Networks.

This work was supported by the National Natural Science Foundation of China (No. U1936212).

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References

  1. Luo, W., Qu, Z., Huang, J., Qiu, G.: A novel method for detecting cropped and recompressed image block. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 217–220 (2007)

    Google Scholar 

  2. Li, W., Yuan, Y., Yu, N.: Passive detection of doctored JPEG image via block artifact grid extraction. Signal Process. 89(9), 1821–1829 (2009)

    Article  Google Scholar 

  3. Christlein, V., Riess, C., Jordan, J., Riess, C., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7(6), 1841–1854 (2012)

    Article  Google Scholar 

  4. Fridrich, A.J., Soukal, B.D., Lukáš, A.J.: Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop (2003)

    Google Scholar 

  5. Huynh, K.T., Ly, T.N., Nguyen, P.T.: Improving the accuracy in copy-move image detection: a model of sharpness and blurriness. SN Comput. Sci. 2(4), 1–11 (2021)

    Article  Google Scholar 

  6. Barni, M., Phan, Q.T., Tondi, B.: Copy move source-target disambiguation through multi-branch CNNs. IEEE Trans. Inf. Forensics Secur. 16, 1825–1840 (2020)

    Article  Google Scholar 

  7. Bianchi, T., Piva, A.: Reverse engineering of double JPEG compression in the presence of image resizing. In: IEEE International Workshop on Information Forensics and Security, pp. 127–132 (2012)

    Google Scholar 

  8. Nguyen, H.C., Katzenbeisser, S.: Detecting resized double JPEG compressed images-using support vector machine. In: IFIP International Conference on Communications and Multimedia Security, pp. 113–122 (2013)

    Google Scholar 

  9. Peng, A., Wu, Y., Kang, X.: Revealing traces of image resampling and resampling antiforensics. Adv. Multimed. 2017, 7130491 (2017)

    Article  Google Scholar 

  10. Chen, Z., Zhao, Y., Ni, R.: Detection of operation chain: JPEG-resampling-JPEG. Signal Process. Image Commun. 57, 8–20 (2017)

    Article  Google Scholar 

  11. Bianchi, T., Piva, A.: Detection of nonaligned double JPEG compression based on integer periodicity maps. IEEE Trans. Inf. Forensics Secur. 7(2), 842–848 (2011)

    Article  Google Scholar 

  12. Pevny, T., Fridrich, J.: Detection of double-compression in JPEG images for applications in steganography. IEEE Trans. Inf. Forensics Secur. 3(2), 247–258 (2008)

    Article  Google Scholar 

  13. Huang, F., Huang, J., Shi, Y.Q.: Detecting double JPEG compression with the same quantization matrix. IEEE Trans. Inf. Forensics Secur. 5(4), 848–856 (2010)

    Article  Google Scholar 

  14. Park, J., Cho, D., Ahn, W., Lee, H.K.: Double JPEG detection in mixed JPEG quality factors using deep convolutional neural network. In: Proceedings of the European Conference on Computer Vision, pp. 636–652 (2018)

    Google Scholar 

  15. Bianchi, T., Piva, A.: Image forgery localization via block-grained analysis of JPEG artifacts. IEEE Trans. Inf. Forensics Secur. 7(3), 1003–1017 (2012)

    Article  Google Scholar 

  16. Chen, Y.L., Hsu, C.T.: Image tampering detection by blocking periodicity analysis in JPEG compressed images. In: IEEE Workshop on Multimedia Signal Processing, pp. 803–808 (2008)

    Google Scholar 

  17. Ye, S., Sun, Q., Chang, E.C.: Detecting digital image forgeries by measuring inconsistencies of blocking artifact. In: IEEE International Conference on Multimedia and Expo, pp. 12–15 (2007)

    Google Scholar 

  18. Zhan, F., Lu, S., Xue, C.: Verisimilar image synthesis for accurate detection and recognition of texts in scenes. In: Proceedings of the European Conference on Computer Vision, pp. 249–266 (2018)

    Google Scholar 

  19. Dundar, A., Sapra, K., Liu, G., Tao, A., Catanzaro, B.: Panoptic-based image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8070–8079 (2020)

    Google Scholar 

  20. Zhan, F., Zhu, H., Lu, S.: Spatial fusion GAN for image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3653–3662 (2019)

    Google Scholar 

  21. Fan, Z., De Queiroz, R.L.: Identification of bitmap compression history: JPEG detection and quantizer estimation. IEEE Trans. Image Process. 12(2), 230–235 (2003)

    Article  Google Scholar 

  22. Chen, Y.L., Hsu, C.T.: Detecting recompression of JPEG images via periodicity analysis of compression artifacts for tampering detection. IEEE Trans. Inf. Forensics Secur. 6(2), 396–406 (2011)

    Article  Google Scholar 

  23. Chen, Z., Tondi, B., Li, X., Ni, R., Zhao, Y., Barni, M.: Secure detection of image manipulation by means of random feature selection. IEEE Trans. Inf. Forensics Secur. 14(9), 2454–2469 (2019)

    Article  Google Scholar 

  24. Holub, V., Fridrich, J.: Low-complexity features for JPEG steganalysis using undecimated DCT. IEEE Trans. Inf. Forensics Secur. 10(2), 219–228 (2014)

    Article  Google Scholar 

  25. Chen, Z., Zhao, Y., Ni, R.: Detection for operation chain: Histogram equalization and dither-like operation. KSII Trans. Internet Inf. Syst. 9(9), 3751–3770 (2015)

    Google Scholar 

  26. Kirchner, M., Bohme, R.: Hiding traces of resampling in digital images. IEEE Trans. Inf. Forensics Secur. 3(4), 582–592 (2008)

    Article  Google Scholar 

  27. Barni, M., Bondi, L., Bonettini, N., Bestagini, P., Costanzo, A., Maggini, M., Tondi, B., Tubaro, S.: Aligned and non-aligned double JPEG detection using convolutional neural networks. J. Vis. Commun. Image Represent. 49, 153–163 (2017)

    Article  Google Scholar 

  28. Liao, X., Li, K., Zhu, X., Liu, K.R.: Robust detection of image operator chain with two-stream convolutional neural network. IEEE J. Sel. Topics Signal Process. 14(5), 955–968 (2020)

    Article  Google Scholar 

  29. Conotter, V., Comesana, P., Pérez-González, F.: Forensic detection of processing operator chains: recovering the history of filtered JPEG images. IEEE Trans. Inf. Forensics Secur. 10(11), 2257–2269 (2015)

    Article  Google Scholar 

  30. Barni, M., Costanzo, A., Sabatini, L.: Identification of cut & paste tampering by means of double-JPEG detection and image segmentation. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp. 1687–1690 (2010)

    Google Scholar 

  31. Li, Y., Zhou, J.: Fast and effective image copy-move forgery detection via hierarchical feature point matching. IEEE Trans. Inf. Forensics Secur. 14(5), 1307–1322 (2018)

    Article  Google Scholar 

  32. Sun, W., Zhou, J., Lyu, R., Zhu, S.: Processing-aware privacy-preserving photo sharing over online social networks. In: Proceedings of the 24th ACM International Conference on Multimedia, pp. 581–585 (2016)

    Google Scholar 

  33. Schaefer, G., Stich, M.: UCID: an uncompressed color image database. Storage Retrieval Meth. Appl. Multimed. 2004(5307), 472–480 (2003)

    Google Scholar 

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Authors and Affiliations

  1. Institute of Information Science, Beijing Jiaotong University, Beijing, 100044, China

    Rongrong Gao, Xiaolong Li & Yao Zhao

  2. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University, Beijing, 100044, China

    Rongrong Gao, Xiaolong Li & Yao Zhao

Authors
  1. Rongrong Gao
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  2. Xiaolong Li
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  3. Yao Zhao
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Corresponding author

Correspondence to Yao Zhao .

Editor information

Editors and Affiliations

  1. University of Science and Technology Beijing, Beijing, China

    Huimin Ma

  2. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Tsinghua University, Beijing, China

    Changshui Zhang

  4. Zhejiang University, Hangzhou, China

    Fei Wu

  5. Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hunan University, Changsha, China

    Yaonan Wang

  7. Sun Yat-Sen University, Guangzhou, Guangdong, China

    Jianhuang Lai

  8. Beijing Jiaotong University, Beijing, China

    Yao Zhao

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Gao, R., Li, X., Zhao, Y. (2021). A Novel Method of Cropped Images Forensics in Social Networks. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13020. Springer, Cham. https://doi.org/10.1007/978-3-030-88007-1_53

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  • DOI: https://doi.org/10.1007/978-3-030-88007-1_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88006-4

  • Online ISBN: 978-3-030-88007-1

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