Open AccessIMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network
Author(s) -
Khan Abdullah Ayub,
Shaikh Aftab Ahmed,
Cheikhrouhou Omar,
Laghari Asif Ali,
Rashid Mamoon,
Shafiq Muhammad,
Hamam Habib
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12272
Subject(s) - computer science , steganography , least significant bit , information hiding , encryption , cover (algebra) , convolutional neural network , cryptography , robustness (evolution) , steganography tools , artificial intelligence , computer vision , steganalysis , image (mathematics) , multimedia , data mining , computer security , mechanical engineering , biochemistry , chemistry , engineering , gene , operating system
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.