Results 81 to 90 of about 5,168 (229)

RIHINNet: A robust image hiding method against JPEG compression based on invertible neural network

IET Image Processing, Volume 18, Issue 13, Page 3801-3814, 13 November 2024.
The loss function is designed to hide the secret image as much as possible in the area that will not change much after JPEG compression, which is helpful to improve the robustness of the model against JPEG compression. The current image‐hiding model fails to converge when it is trained robustly for all compression quality factors. Through the design of
Xin Jin, Chengyi Pan, Zien Cheng, Yunyun Dong, Qian Jiang   +4 more
wiley   +1 more source

Comprehensive survey on image steganalysis using deep learning

Array
Steganalysis, a field devoted to detecting concealed information in various forms of digital media, including text, images, audio, and video files, has evolved significantly over time.
Ntivuguruzwa Jean De La Croix, Tohari Ahmad, Fengling Han   +2 more
doaj   +1 more source

Hiding image with inception transformer

IET Image Processing, Volume 18, Issue 13, Page 3961-3975, 13 November 2024.
In this article, we proposed a novel image steganography framework called HiiT, which takes advantage of CNN and Transformer to learn both the local and global pixel correlation in image hiding. Specifically, we propose a new Transformer structure called Inception Transformer, which incorporates the Inception Net in the attention‐based Transformer ...
Yunyun Dong, Ping Wei, Ruxin Wang, Bingbing Song, Tingchu Wei, Wei Zhou   +5 more
wiley   +1 more source

Stegocrypt: A robust tri‐stage spatial steganography algorithm using TLM encryption and DNA coding for securing digital images

IET Image Processing, Volume 18, Issue 13, Page 4189-4206, 13 November 2024.
This research work presents a novel secured spatial steganography algorithm consisting of three stages. In the first stage, a secret message is divided into three parts, each is encrypted using a tan logistic map encryption key with a unique seed value.
Wassim Alexan, Eyad Mamdouh, Amr Aboshousha, Youssef S. Alsahafi, Mohamed Gabr, Khalid M. Hosny   +5 more
wiley   +1 more source

IAS-CNN: Image adaptive steganalysis via convolutional neural network combined with selection channel

International Journal of Distributed Sensor Networks, 2020
Steganography is conducive to communication security, but the abuse of steganography brings many potential dangers. And then, steganalysis plays an important role in preventing the abuse of steganography.
Zhujun Jin, Yu Yang, Yuling Chen, Yuwei Chen   +3 more
doaj   +1 more source

Information hiding using approximate POSIT representation

IET Image Processing, Volume 18, Issue 13, Page 4221-4249, 13 November 2024.
Ensure security level by hiding data using POSIT and AC at LSB of binary representation and floating‐point value to achieve higher accuracy as compared to IEEE‐754 data format and binary number representation. Enhance the security of hiding data into pixels of black/white and ARGB images by employing two options; option 1 utilizes one digit from each ...
Sa'ed Abed, Ghadeer Aldamkhi, Imtiaz Ahmad   +2 more
wiley   +1 more source

Retraction Note to: Feature reduced blind steganalysis using DCT and spatial transform on JPEG images with and without cross validation using ensemble classifiers [PDF]

, 2022
M. G. Gireeshan, Deepa D. Shankar, Adresya Suresh Azhakath   +2 more
openalex   +1 more source

Image steganalysis based on CNN-Transformer

Shenzhen Daxue xuebao. Ligong ban
Current convolutional neural network (CNN) steganalysis models primarily focus on the local features of steganographic images. Although CNNs expand their receptive field by stacking deeper convolutional layers, their ability to extract global features ...
WANG Jiahao, YAN Hongcan, GU Jiantao
doaj   +1 more source

Strategy to improve the accuracy of convolutional neural network architectures applied to digital image steganalysis in the spatial domain. [PDF]

PeerJ Comput Sci, 2021
Tabares-Soto R, Arteaga-Arteaga HB, Mora-Rubio A, Bravo-Ortíz MA, Arias-Garzón D, Alzate Grisales JA, Burbano Jacome A, Orozco-Arias S, Isaza G, Ramos Pollan R.   +9 more
europepmc   +1 more source

LSSD: A Controlled Large JPEG Image Database for Deep-Learning-Based Steganalysis “Into the Wild” [PDF]

, 2021
Hugo Ruiz, Mehdi Yedroudj, Marc Chaumont, Frédéric Comby, Gérard Subsol   +4 more
openalex   +1 more source