{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T19:34:27Z","timestamp":1694633667865},"reference-count":13,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2008,10,17]],"date-time":"2008-10-17T00:00:00Z","timestamp":1224201600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008,10,17]]},"abstract":"Purpose<\/jats:title>The purpose of this paper is to present research results in analyzing image contents to improve the accuracy of using an artificial neural network (ANN) to detect embedded data in a digital image.<\/jats:p><\/jats:sec>Design\/methodology\/approach<\/jats:title>A texture measure based on the MPEG\u20107 texture descriptor is applied to assess the local texture amount. Those image blocks with high texture are masked out and the remaining blocks with low texture are used to derive features for an ANN to classify an image as embedded or clear. The high\u2010texture blocks are not discarded and can be tested independently for embedded data.<\/jats:p><\/jats:sec>Findings<\/jats:title>By masking out the high\u2010texture image blocks, an ANN has improved detection performance especially when the original embedding rate is low. Bypassing the low\u2010texture image blocks do not pay off for a steganographer because the effective embedding rate in the high\u2010texture blocks is driven higher.<\/jats:p><\/jats:sec>Research limitations\/implications<\/jats:title>Hidden data detectors should take the image content into account in order to improve detection performance.<\/jats:p><\/jats:sec>Practical implications<\/jats:title>The results can be integrated into a steganalytic system.<\/jats:p><\/jats:sec>Originality\/value<\/jats:title>This paper presents evidence that image texture affects steganalytic performance and proposes a solution that incorporates texture that has improved detection performance.<\/jats:p><\/jats:sec>","DOI":"10.1108\/17563780810919122","type":"journal-article","created":{"date-parts":[[2008,11,22]],"date-time":"2008-11-22T12:07:10Z","timestamp":1227355630000},"page":"549-562","source":"Crossref","is-referenced-by-count":2,"title":["Texture\u2010based image steganalysis by artificial neural networks"],"prefix":"10.1108","volume":"1","author":[{"given":"Michael A.","family":"Pratt","sequence":"first","affiliation":[]},{"given":"Sharath","family":"Konda","sequence":"additional","affiliation":[]},{"given":"Chee\u2010Hung","family":"Henry Chu","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022020920245748200_b1","doi-asserted-by":"crossref","unstructured":"Avcibas, I., Memon, N. and Sankur, B. (2003), \u201cSteganalysis using image quality metrics\u201d, IEEE Transactions on Image Processing, Vol. 12 No. 2, pp. 221\u20109.","DOI":"10.1109\/TIP.2002.807363"},{"key":"key2022020920245748200_b2","unstructured":"Benton, R.G. and Chu, C.H. (2005), \u201cSoft computing approach to steganalysis of digital images\u201d, Proc. IEEE Int. Conference on Information Technology: Research and Education. Hsinchu, Taiwan."},{"key":"key2022020920245748200_b8","doi-asserted-by":"crossref","unstructured":"Daniel, L. and Peters, J.F. (2008), \u201cAdaptive learning by a target\u2010tracking system\u201d, International Journal of Intelligent Computing and Cybernetics, Vol. 11, pp. 46\u201068.","DOI":"10.1108\/17563780810857121"},{"key":"key2022020920245748200_b3","unstructured":"Farid, H. (2002), \u201cDetecting hidden messages using higher\u2010order statistical models\u201d, Proc. IEEE Int. Conf. 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