Abstract
Image mining has a very emerging sub-domain, namely, web image mining, and researchers are warmly excited towards it. This study presents a deep detail of former studies and ideas that we come up with, i.e. what is Image Mining and what are Web Image Mining techniques. In the domain of web image mining, this study also proposes an idea to recognize and cope with the deceptive images found on the web. It further helps in banning the fraudulent and annoying web users along with solutions in enhancing the users’ behavior in social networking websites like Facebook, Twitter, Tumbler, etc., in blogs, and in e-shopping websites like eBay, Amazon, Daraz.pk, Kaymu.pk, etc. Apart from this, the study also mentions nearly of the conceivable future directions for the researchers in aforementioned domain.
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Acknowledgments
This work is dedicated to our parents who are the reason for us being at this point in our studies and to teachers/advisors who made our basic concepts clear enough for our efforts to be put in such a presentable form. We thank them both for encouraging us toward the research in this domain.
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Zafar, S., Irum, N., Arshad, S., Nawaz, T. (2019). Spam User Detection Through Deceptive Images in Big Data. In: Jan, M., Khan, F., Alam, M. (eds) Recent Trends and Advances in Wireless and IoT-enabled Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-99966-1_28
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DOI: https://doi.org/10.1007/978-3-319-99966-1_28
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