Abstract
The Internet nowadays is exploded with digital images. However, it also has more forged images floating around and threatening the digital asset trustworthy, especially spliced images. Although there are many computational ways of detecting image splicing, the image splicing technique is advancing over time, driving feature specific algorithms to obsolete. Thus, more researchers nowadays are focusing on data-driven feature extraction. Convolutional Neural Network (CNN) is a Deep Learning algorithm well known for object detection, but it is ill-suited for manipulation feature extraction. In this research, a constrained convolution algorithm is to be injected into a simple CNN to study its performance in image splicing detection in a wide range of datasets. A strategic parameter tweaking can fit a wide range of spliced image datasets, but it is always biased to its train dataset. A cross-database splicing classification shows that it is ill-suited for generalizing a wide range of dataset evaluation.
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References
Bayar, B., Stamm, M.C.: A deep learning approach to universal image manipulation detection using a new convolutional layer, pp. 5–10 (2016)
Bayar, B., Stamm, M.C.: Constrained convolutional neural networks: a new approach towards general purpose image manipulation detection. IEEE Trans. Inf. Forensics Secur. 13(11), 2691–2706 (2018)
Zhan L, Yuesheng Z.: Passive forensics for image splicing based on PCA noise estimation. In: 10th International Conference for Internet Technology and Secured Transactions (ICITST), pp. 78–83 (2015)
Zhang Y, Goh J, Win LL, Thing V.: Image region forgery detection: a deep learning approach. In: Cryptology and Information Security Series, pp. 1–11 (2016)
Vaishnavi D.: Recognizing image splicing forgeries using histogram features. In: 3rd MEC International Conference on Big Data and Smart City, pp. 1–4 (2016)
He Z, Lu W, Sun W, Huang J.: Digital image splicing detection based on Markov features in DCT and DWT domain. In: Pattern Recognition, pp. 4292–4299. Elsevier (2012)
Rao Y, Ni J.: A deep learning approach to detection of splicing and copy-move forgeries in images. In: IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6 (2016)
Rao, Y., Ni, J., Zhao, H.: Deep learning local descriptor for image splicing detection and localization. IEEE Access 8, 25611–25625 (2020)
Salloum, R., Yuzhuo Ren, C.-C., Kuo, J.: Image splicing localization using a multi-task fully convolutional network (MFCN). J. Vis. Commun. Image Represent. 51, 201–209 (2018)
Liu, Y., Zhao, X.: Constrained image splicing detection and localization with attention-aware encoder-decoder and atrous convolution. IEEE Access 8, 6729–6741 (2020)
Phan-xuan, H., Le-Tien, T., Nguyen-Chinh, T., et al.: Preserving spatial information to enhance performance of image forgery classification. In: 2019 International Conference on Advanced Technologies for Communications (ATC), pp. 50–55. IEEE (2019)
Cozzolino, D., Verdoliva, L.: Single-image splicing localization through autoencoder-based anomaly detection. In: 8th IEEE International Workshop on Information Forensics and Security, WIFS 2016 (2017)
Verdoliva, L., Cozzolino, D., Poggi, G.: A feature-based approach for image tampering detection and localization. In: 2014 IEEE International Workshop on Information Forensics and Security, WIFS 2014, pp. 149–154 (2015)
Cozzolino, D., Poggi, G., Verdoliva, L.: Recasting residual-based local descriptors as convolutional neural networks: an application to image forgery detection. In: IH&MMSec 2017, pp. 159–164 (2017)
Bunk, J., Bappy, J.H., Mohammed, T.M., et al.: Detection and localization of image forgeries using resampling features and deep learning. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1881–1889 (2017)
Diallo, B., Urruty, T., Bourdon, P., Fernandez-Maloigne, C.: Robust forgery detection for compressed images using CNN supervision. Forensic Sci. Int. Rep. 2, 100112 (2020)
Cozzolino, D., Verdoliva, L.: Noiseprint: a CNN-based camera model fingerprint. IEEE Trans. Inf. Forensics Secur. 15, 144–159 (2020)
Zhao, X., Li, J., Li, S., Wang, S.: Detecting digital image splicing in chroma spaces. In: Kim, H.-J., Shi, Y.Q., Barni, M. (eds.) IWDW 2010. LNCS, vol. 6526, pp. 12–22. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-18405-5_2
De Carvalho, T.J., Riess, C., Angelopoulou, E., et al.: Exposing digital image forgeries by illumination color classification. IEEE Trans. Inf. Forensics Secur. 8, 1182–1194 (2013)
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The author would like to acknowledge the Ministry of Education Malaysia’s financial assistance through FRGS grant number 203/PELECT/6071305 and Universiti Sains Malaysia through RUI grant number 101/PELECT/8014111.
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Lee, Y.Y., Kong, T.S., Khoo, B.E. (2022). Behavioural Study of Constrained Convolutional Neural Network on Image Splicing Classification of Various Datasets. In: Mahyuddin, N.M., Mat Noor, N.R., Mat Sakim, H.A. (eds) Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 829. Springer, Singapore. https://doi.org/10.1007/978-981-16-8129-5_73
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