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Steganographic-algorithm and length estimation classification on MP3 steganalysis with convolutional neural network
Duwinanto M.R.a, Munir R.a
a Bandung Institute of Technology, School of Electrical Engineering and Informatics, Bandung, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.Steganography is a method of embedding secret messages into a cover file in the form of text, audio, picture or video, so that the message is not suspected by those who are not authorized to open the message. The technique to find out whether the cover media is a stego file or not is steganalysis. In this study, detection of hidden messages focused on MP3 files inserted by the MP3Stego algorithm and Equal Length Entropy Codes Substitution to classify based on algorithms and the estimated length of the message and detect cover files. In conducting this research, it is necessary to know the audio features of MP3, build suitable deep learning methods and the performance of the models that have been produced. The proposed solution for these two problems is to use the QMDCT audio feature and deep learning architecture with Convolutional Neural Network. The results of this study are the best algorithm classification model with an accuracy performance of 91.78% and F1-Score 92.22% and the best classification model for message length estimation has an accuracy performance of 24.16% and F1-Score 21.40%. Thus, the proposal of deep learning architecture is good in classifying algorithms and covers, but still poor in classifying the estimated length of the message.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Classification models,Hidden messages,Learning architectures,Learning methods,Length estimation,Message length estimations,Secret messages,Steganographic algorithms[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Classification,Convolutional Neural Network,MP3,Steganalysis,Steganography[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICITISEE48480.2019.9004022[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]