Image Steganalysis based on Pretrained Convolutional Neural Networks
Decision 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;...
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Institute of Electrical and Electronics Engineers Inc.
2023
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my.uniten.dspace-271752023-05-29T17:40:31Z Image Steganalysis based on Pretrained Convolutional Neural Networks Taha Ahmed I. Tareq Hammad B. Jamil N. 57193324906 57763080600 36682671900 Decision 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 networks the 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. Final 2023-05-29T09:40:31Z 2023-05-29T09:40:31Z 2022 Conference Paper 10.1109/CSPA55076.2022.9782061 2-s2.0-85132734201 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132734201&doi=10.1109%2fCSPA55076.2022.9782061&partnerID=40&md5=d7387202a07b6ac8946a1b0c966dd309 https://irepository.uniten.edu.my/handle/123456789/27175 283 286 Institute of Electrical and Electronics Engineers Inc. Scopus |
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| description |
Decision 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 networks |
| author2 |
57193324906 |
| author_facet |
57193324906 Taha Ahmed I. Tareq Hammad B. Jamil N. |
| format |
Conference Paper |
| author |
Taha Ahmed I. Tareq Hammad B. Jamil N. |
| spellingShingle |
Taha Ahmed I. Tareq Hammad B. Jamil N. Image Steganalysis based on Pretrained Convolutional Neural Networks |
| author_sort |
Taha Ahmed I. |
| title |
Image Steganalysis based on Pretrained Convolutional Neural Networks |
| title_short |
Image Steganalysis based on Pretrained Convolutional Neural Networks |
| title_full |
Image Steganalysis based on Pretrained Convolutional Neural Networks |
| title_fullStr |
Image Steganalysis based on Pretrained Convolutional Neural Networks |
| title_full_unstemmed |
Image Steganalysis based on Pretrained Convolutional Neural Networks |
| title_sort |
image steganalysis based on pretrained convolutional neural networks |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806424351228035072 |
| score |
13.214268 |