Publication:
Image Steganalysis based on Pretrained Convolutional Neural Networks

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dc.citedby1
dc.contributor.authorTaha Ahmed I.en_US
dc.contributor.authorTareq Hammad B.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorid57193324906en_US
dc.contributor.authorid57763080600en_US
dc.contributor.authorid36682671900en_US
dc.date.accessioned2023-05-29T09:40:31Z
dc.date.available2023-05-29T09:40:31Z
dc.date.issued2022
dc.descriptionDecision trees; Deep learning; Engineering education; Image classification; Learning systems; Steganography; Alexnet CNN model; CNN models; Convolutional neural network; Cover-image; Image steganalysis; Random forest classifier istego100k; Random forest classifier; Secret information; Steganalysis; Stego image; Convolutional neural networksen_US
dc.description.abstractthe process of identifying the presence of secret information in cover images is known as image steganalysis. As a result, classifying an image as a cover image or a stego image might be considered a classification task. The majority of steganalysis approaches that rely on deep learning are effective. Deep learning technology can identify and extract features mechanically using deep networks, allowing steganalysis technology to eliminate the need for specialist knowledge. However, Deep learning model training is tough and takes a large amount of processing time and information. Therefore, pre-Trained CNN such as AlexNet model were used as feature extractors to save time during training. Therefore, this research presented an image steganalysis method based on AlexNet CNN Model. There are 3 steps make up the proposed image steganalysis method: Firstly, Data collection and preparation. Secondly, AlexNet model are used for extract Distinctive features. Lastly, the feature vector is then utilized to train the Random forest (RF) classifier in order to detect the binary classification (Cover/Stego). The experimental results under IStego100K database show that the proposed method accuracy is 99%. The properties of AlexNet models can be deduced to be useful and concise to classify using RF. In compared to previous techniques, the presented method outperformed them. � 2022 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/CSPA55076.2022.9782061
dc.identifier.epage286
dc.identifier.scopus2-s2.0-85132734201
dc.identifier.spage283
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85132734201&doi=10.1109%2fCSPA55076.2022.9782061&partnerID=40&md5=d7387202a07b6ac8946a1b0c966dd309
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27175
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2022 IEEE 18th International Colloquium on Signal Processing and Applications, CSPA 2022 - Proceeding
dc.titleImage Steganalysis based on Pretrained Convolutional Neural Networksen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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