IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network Article Swipe
Abdullah Ayub Khan
,
Aftab Ahmed Shaikh
,
Omar Cheikhrouhou
,
Asif Ali Laghari
,
Mamoon Rashid
,
Muhammad Shafiq
,
Habib Hamam
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1049/ipr2.12272
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1049/ipr2.12272
Information hiding aims to embed a crucial amount of confidential data records into the multimedia, such as text, audio, static and dynamic image, and video. Image‐based information hiding has been a significantly important topic for digital forensics. Here, active image deep steganographic approaches have come forward for hiding data. The least significant bit (LSB) steganography approach is proposed to conceal a secret message into the original image. First, the lightweight stream encryption cryptography encrypts secret information in the cover image to protect embedded information from source to destination. Whereas the encrypted embedded cover information into the carrier of stego‐image with the help of the LSB and then transmit. In the proposed investigational scheme, a convolutional neural net is used. A model is trained to detect and extract patterns of image hidden features, encrypted stego‐image optimization, and classify original and cover images of steganography. Through the experiment result on the forensic image database for mobile steganography of the Center for Statistics and Application in Forensic Evidence, the overall embedded and extracting that the proposed scheme can achieve information hiding as well as revealing with an accuracy rate of 95.1%. The experimental result shows the robustness of the model in terms of efficiency as compared to other state‐of‐the‐art schemes.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/ipr2.12272
- OA Status
- gold
- Cited By
- 45
- References
- 28
- Related Works
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- OpenAlex ID
- https://openalex.org/W3164190449
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3164190449Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1049/ipr2.12272Digital Object Identifier
- Title
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IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural networkWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-05-24Full publication date if available
- Authors
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Abdullah Ayub Khan, Aftab Ahmed Shaikh, Omar Cheikhrouhou, Asif Ali Laghari, Mamoon Rashid, Muhammad Shafiq, Habib HamamList of authors in order
- Landing page
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https://doi.org/10.1049/ipr2.12272Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1049/ipr2.12272Direct OA link when available
- Concepts
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Computer science, IMG, Convolutional neural network, Multimedia, Network forensics, Information hiding, Computer network, Artificial intelligence, Computer security, Digital forensics, Image (mathematics), Operating systemTop concepts (fields/topics) attached by OpenAlex
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45Total citation count in OpenAlex
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2025: 6, 2024: 13, 2023: 5, 2022: 13, 2021: 7Per-year citation counts (last 5 years)
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28Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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