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Toward effective image forensics via a novel computationally efficient framework and a new image splice dataset

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Abstract

Splice detection models are the need of the hour since splice manipulations can be used to mislead, spread rumors and create disharmony in society. However, there is a severe lack of image-splicing datasets, which restricts the capabilities of deep learning models to extract discriminative features without overfitting. This manuscript presents twofold contributions toward splice detection. Firstly, a novel splice detection dataset is proposed having two variants. The two variants include spliced samples generated from code and through manual editing. Spliced images in both variants have corresponding binary masks to aid localization approaches. Secondly, a novel spatio-compression lightweight splice detection framework is proposed for accurate splice detection with minimum computational cost. The proposed dual-branch framework extracts discriminative spatial features from a lightweight spatial branch. It uses original resolution compression data to extract double compression artifacts from the second branch, thereby making it ‘information preserving.’ Several CNNs are tested in combination with the proposed framework on a composite dataset of images from the proposed dataset and the CASIA v2.0 dataset. The best model accuracy of 0.9382 is achieved and compared with similar state-of-the-art methods, demonstrating the superiority of the proposed framework.

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Data availability

The datasets generated during and/or analyzed during the current study are available through an online web repository via the following weblinks: CASIA v2.0-https://www.kaggle.com/datasets/divg07/casia-20-image-tampering-detection-dataset

References

  1. Dean, B.: Social network usage & growth statistics: how many people use social media in 2021?," BackLinko [Online]. (2021). https://backlinko.com/social-media-users

  2. Ahmad, M., Khursheed, F.: Detection and localization of image tampering in digital images with fused features. Concurr. Comput.: Pract. Exp. 34(23), e7191 (2022)

    Article  Google Scholar 

  3. Singla, N., Singh, J., Nagpal, S.: Raven finch optimized deep convolutional neural network model for intra-frame video forgery detection. Concurr. Comput.: Pract. Exp. 35(3), e7516 (2023)

    Article  Google Scholar 

  4. Cristin, R., Premnath, S.P., Ananth, J.P.: Image tampering detection in image forensics using earthworm-rider optimization. Concurr. Comput.: Pract. Exp. 34(26), e7293 (2022)

    Article  Google Scholar 

  5. Chen, C., McCloskey, S. and Yu, J.: Image splicing detection via camera response function analysis. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu (2017)

  6. Pomari, T., Ruppert, G., Rezende, E., Rocha, A. and Carvalho, T.: Image Splicing detection through illumination inconsistencies and deep learning. In: IEEE International Conference on Image Processing (ICIP), Athens (2018)

  7. Salloum, R., Ren, Y., Kuo, C.-C.J.: Image splicing localization using a multi-task fully convolutional network (MFCN). J. Vis. Commun. Image Represent. 51, 201–209 (2018)

    Article  Google Scholar 

  8. Cun, X. and Pun, C.M.: Image splicing localization via semi-global network and fully connected conditional random fields. In: European Conference on Computer Vision (ECCV), Munich (2018)

  9. Liu, B. and Pun, C.M.: Deep fusion network for splicing forgery localization. In: European Conference on Computer Vision (ECCV), Munich (2018)

  10. Mazumdar, A. and Bora, P.K.: Deep learning-based classification of illumination maps for exposing face splicing forgeries in images. In: 2019 IEEE International Conference on Image Processing (ICIP), Taipei (2019)

  11. Bi, X., Wei, Y., Xiao, B. and Li, W.: RRU-Net: the ringed residual u-net for image splicing forgery detection. In: IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW), Long Beach (2019)

  12. Deng, C., Li, Z., Gao, X., Tao, D.: Deep multi-scale discriminative networks for double JPEG compression forensics. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1–20 (2019)

    Article  Google Scholar 

  13. Horváth, J., Montserrat, D.M., Hao, H. and Delp, E.J.: Manipulation detection in satellite images using deep belief networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle (2020)

  14. Xiao, B., Wei, Y., Bi, X., Li, W., Ma, J.: Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering. Inf. Sci. 511, 172–191 (2020)

    Article  MathSciNet  Google Scholar 

  15. Wang, J., Ni, Q., Liu, G., Luo, X., Jha, S.K.: Image splicing detection based on convolutional neural network with weight combination strategy. J. Inf. Secur Appl. 54, 102523 (2020)

    Google Scholar 

  16. Liu, B., Pun, C.-M.: Exposing splicing forgery in realistic scenes using deep fusion network. Inf. Sci. 526, 133–150 (2020)

    Article  MathSciNet  Google Scholar 

  17. Simonyan, K. and Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR), San Diego, (2015)

  18. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston (2015)

  19. He, K., Zhang, X., Ren, S. and Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas (2016)

  20. Huang, G., Liu, Z., Van Der Maaten, L. and Weinberger, K.Q.:Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu (2017)

  21. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S. and Uszkoreit, J.: An image is worth 16 × 16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (ICLR), Austria (2021)

  22. Li, D., Hu, J., Wang, C., Li, X., She, Q., Zhu, L., Zhang, T. and Chen, Q.: Involution: inverting the inherence of convolution for visual recognition. In: Computer Vision and Pattern Recognition (CVPR), Nashville (2021)

  23. Wang, Q., Zhang, R.: Double JPEG compression forensics based on a convolutional neural network. EURASIP J. Inf. Secur. 2016(1), 1–12 (2016)

    Google Scholar 

  24. Amerini, I., Uricchio, T., Ballan, L. and Caldelli, R.: Localization of JPEG double compression through multi-domain convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu (2017)

  25. Nguyen, D.T., Pasquini, C., Conotter, V. and Boato, G.: RAISE: a raw images dataset for digital image forensics. In: 6th ACM Multimedia Systems Conference, Portland (2015)

  26. Ng, T.T., Hsu, J. and Chang, S.F.: Columbia image splicing detection evaluation dataset. Columbia University (2004). [Online]. Available: https://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/AuthSplicedDataSet.htm.

  27. Hsu, Y.F. and Chang, S.F.: Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency. In: IEEE International Conference on Multimedia and Expo, Toronto (2006)

  28. Dong, J., Wang, W. and Tan, T.: CASIA Image Tampering Detection Evaluation Database. In: IEEE China Summit and International Conference on Signal and Information Processing, Beijing (2013)

  29. Carvalho, T.J., Riess, C., Angelopoulou, E., Pedrini, H., de Rezende Rocha, A.: Exposing digital image forgeries by illumination color classification. IEEE Trans. Inf. Forensics Secur. 8(7), 1182–1194 (2013)

    Article  Google Scholar 

  30. Image Forensics Challenge Dataset (2014). [Online]. Available: https://signalprocessingsociety.org/newsletter/2014/01/ieee-ifs-tc-image-forensics-challenge-website-new-submissions

  31. Chen, S., Tan, S., Li, B., Huang, J.: Automatic detection of object-based forgery in advanced video. IEEE Trans. Circuits Syst. Video Technol. 26(11), 2138–2151 (2016)

    Article  Google Scholar 

  32. Cozzolino, D., Poggi, G., Verdoliva, L.: Efficient dense-field copy-move forgery detection. IEEE Trans. Inf. Forensics Secur. 10(11), 2284–2297 (2015)

    Article  Google Scholar 

  33. Zhang, Y., Zhu, G., Wu, L., Kwong, S., Zhang, H., Zhou, Y.: Multi-task SE-Network for image splicing localization. IEEE Trans. Circuits Syst. Video Technol. 32(7), 4828–4840 (2022)

    Article  Google Scholar 

  34. Sun, Y., Ni, R., Zhao, Y.: ET: edge-enhanced transformer for image splicing detection. IEEE Signal Process. Lett. 29, 1232–1236 (2022)

    Article  Google Scholar 

  35. Yan, C., Li, S., Li, H.: TransU2-Net: a hybrid transformer architecture for image splicing forgery detection. IEEE Access 11, 33313–33323 (2023)

    Article  Google Scholar 

  36. Wu, Y., AbdAlmageed, W. and Natarajan, P.: ManTra-Net: manipulation tracing network for detection and localization of image forgeries with anomalous features. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach (2019)

  37. Chen, B., Qi, X., Wang, Y., Zheng, Y., Shim, H.J., Shi, Y.-Q.: An improved splicing localization method by fully convolutional networks. IEEE Access 6, 69472–69480 (2018)

    Article  Google Scholar 

  38. Liu, B., Pun, C.-M.: Locating splicing forgery by fully convolutional networks and conditional random field. Signal Process.: Image Commun. 66, 103–112 (2018)

    Google Scholar 

  39. Wu, Y., Wo, Y., Han, G.: Joint manipulation trace attention network and adaptive fusion mechanism for image splicing forgery localization. Multimed. Tools Appl. 81, 38757–38780 (2022)

    Article  Google Scholar 

  40. Chen, X., Dong, C., Ji, J., Cao, J. and Li, X.: Image Manipulation Detection by Multi-View Multi-Scale Supervision. In: IEEE/CVF International Conference on Computer Vision, Montreal (2021)

  41. Zhang, H., Dana, K., Shi, J., Zhang, Z., Wang, X., Tyagi, A. and Agrawal, A.: Context encoding for semantic segmentation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City (2018)

  42. Xu, D., Shen, X., Huang, Y., Shi, Z.: RB-Net: integrating region and boundary features for image manipulation localization. Multimed. Syst. 29(5), 3055–3067 (2023)

    Article  Google Scholar 

  43. Chen, H., Han, Q., Li, Q., Tong, X.: Digital image manipulation detection with weak feature stream. Vis. Comput. 38, 2675–2689 (2022)

    Article  Google Scholar 

  44. Zhou, P., Han, X., Morariu, V.I. and Davis, L.S.: Learning Rich Features for Image Manipulation Detection. In: Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA (2018)

  45. Wei, X., Wu, Y., Dong, F., Zhang, J., Sun, S.: Developing an image manipulation detection algorithm based on edge detection and faster R-CNN. Symmetry 11(10), 1223 (2019)

    Article  Google Scholar 

  46. Chen, Y., Kang, X., Shi, Y.Q., Wang, Z.J.: A multi-purpose image forensic method using densely connected convolutional neural networks. J. Real-Time Image Proc. 16, 725–740 (2019)

    Article  Google Scholar 

  47. Park, J., Cho, D., Ahn, W. and Lee, H.K.: Double JPEG detection in mixed JPEG quality factors using deep convolutional neural network. In: European Conference on Computer Vision (ECCV), Munich (2018)

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Authors and Affiliations

  1. Department of Information Technology, Delhi Technological University, Delhi, India

    Ankit Yadav & Dinesh Kumar Vishwakarma

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  1. Ankit Yadav
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  2. Dinesh Kumar Vishwakarma
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Contributions

AY contributed to software, validation, investigation, data curation, writing—original draft, and visualization.

DKV contributed to conceptualization, methodology, formal analysis, resources, writing—review & editing, supervision, project administration, and funding acquisition.

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Correspondence to Ankit Yadav.

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Yadav, A., Vishwakarma, D.K. Toward effective image forensics via a novel computationally efficient framework and a new image splice dataset. SIViP 18, 3341–3352 (2024). https://doi.org/10.1007/s11760-024-02997-6

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