Gopi, E. S. Digital image forgery detection using artificial neural network and independent component analysis. (English) Zbl 1193.94018 Appl. Math. Comput. 194, No. 2, 540-543 (2007). Summary: Digital image forgery is the process of manipulating the original photographic images like resizing, rotation, scaling, etc. To produce the photographic images as the evidence to the court, there is the need to identify whether the produced image is original or forgery image. In this paper, an attempt is made to detect forgery portions of the digital image. This is achieved by training the artificial neural network using the ICA coefficients obtained in the AR domain of the image data. Cited in 2 Documents MSC: 94A08 Image processing (compression, reconstruction, etc.) in information and communication theory 68T05 Learning and adaptive systems in artificial intelligence Keywords:auto regressive model; back propagation neural network PDF BibTeX XML Cite \textit{E. S. Gopi}, Appl. Math. Comput. 194, No. 2, 540--543 (2007; Zbl 1193.94018) Full Text: DOI References: [1] Popescu, Alin C.; Farid, Hanry, Exposing digital forgeries by detecting traces of resampling, IEEE Transactions on Signal Processing, 53, 2 (2005) · Zbl 1370.94342 [3] Hyvarinen, A.; Oja, E., Independent component analysis: algorithms and applications, Neural Networks, 13, 4-5, 411-430 (2000) [4] Haykin, Simon, Neural Networks - A Comprehensive Foundation (1999), PearsonEdu., Inc. · Zbl 0934.68076 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.