期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：12
页码：1-7
出版社：Science and Information Society (SAI)
摘要：In this technological era, social media has a major
role in people’s daily life. Most people share text, images, and
videos on social media frequently (e.g. Twitter, Snapchat,
Facebook, and Instagram). Images are one of the most common
types of media share among users on social media. So, there is a
need for monitoring of images contained in social media. It has
become easy for individuals and small groups to fabricate these
images and disseminate them widely in a very short time, which
threatens the credibility of the news and public confidence in the
means of social communication. This research attempted to
propose an approach to extracting image content, classify it and
verify the authenticity of digital images and uncover
manipulation. Instagram is one of the most important websites
and mobile image sharing applications on social media. This
allows users to take photos, add digital photographic filters and
upload pictures. There are many unwanted contents in
Instagram's posts such as threats and forged images, which may
cause problems to society and national security. This research
aims to build a model that can be used to classify Instagram
content (images) to detect any threats and forged images. The
model was built using deep algorithms learning which is
Convolutional Neural Network (CNN), Alexnet network and
transfer learning using Alexnet. The results showed that the
proposed Alexnet network offers more accurate detection of fake
images compared to the other techniques with 97%. The results
of this research will be helpful in monitoring and tracking in the
shared images in social media for unusual content and forged
images detection and to protect social media from electronic
attacks and threats.
关键词：Convolution Neural Network (CNN); Image
forgery; Classification; Alexnet; Rectified Linear Unit (ReLU);
SoftMax function; Features extraction
