Results 51 to 60 of about 271 (187)
ACTIC: A Large Language Model‐Based Method for Threat Intelligence Perception
IET Communications, Volume 20, Issue 1, January/December 2026.This paper proposes a study that introduces an automated method for constructing a threat intelligence knowledge graph construction method based on using large language models, named ACTIC. This approach utilises a locally deployed DeepSeek‐32B model and employs, combined with prompt engineering and Low‐Rank Adaptation (LoRA) fine‐tuning, to extract ...
Changcheng Liu, Changheng Yang, Jun Ma
wiley +1 more source
A Coverless Audio Steganography Based on Generative Adversarial Networks
, 2023 Traditional audio steganography by cover modification causes changes to the cover features during the embedding of a secret, which is easy to detect with emerging neural-network steganalysis tools.
Jing Li, Xiaozhu Jia, Kaixi Wang
core +1 more source
Heat Transfer Calculations in Barrel Cover of 35 mm Naval Armament System Gun [PDF]
Problemy Mechatroniki, 2018 This paper presents the results of numerical simulations of non-stationary heat transfer in a 35 mm anti-air gun barrel cover made of composite materials. The cover protects the gun against weather conditions, including sea water effects, and serves as a
Zbigniew LECIEJEWSKI +5 more
doaj +1 more source
An Improved Near‐Reversible Technique With Residual Networks
IET Image Processing, Volume 19, Issue 1, January/December 2025.The study compares three cover‐image extraction subnetworks with varying residual depths, combining convolutional neural networks (CNNs) and Residual Networks (ResNets) to improve cover‐image reconstruction quality, ensuring high‐fidelity, nearly reversible reconstruction.
Tran Thi Ngoc Tin, Yih‐Chuan Lin
wiley +1 more source
VidaGAN: Adaptive GAN for image steganography
IET Image Processing, Volume 18, Issue 12, Page 3329-3342, 16 October 2024.This study introduces a steganography framework named VarIable aDAptive GAN that utilizes deep learning techniques. It also introduces a novel method for embedding any type of binary data into images using generative adversarial networks, enabling us to enhance the visual appeal of images generated by the specified model.
Vida Yousefi Ramandi +2 more
wiley +1 more source
Coverless Steganography for Digital Images Based on a Generative Model
, 2018 In this paper, we propose a novel coverless image steganographic scheme based on a generative model. In our scheme, the secret image is first fed to the generative model database, to generate a meaning-normal and independent image different from the ...
Haoxian Song +3 more
core +1 more source
Time-based Key for Coverless Audio Steganography: A Proposed Behavioral Method to Increase Capacity
, 2023 Background. Coverless steganography is a relatively unexplored area of steganography where the message is not embedded into a cover media. Instead the message is derived from one or several properties already existing in the carrier media.
Svensson, Carl, Alanko Öberg, John
core +1 more source
AI‐generated video steganography based on semantic segmentation
IET Image Processing, Volume 18, Issue 11, Page 3042-3054, 18 September 2024.This paper proposes an AI‐generated video steganography algorithm based on semantic segmentation and histogram embedding. The stego video generated in the scheme has strong robustness against various noise attacks and security for anti‐steganalysis.
Yangping Lin +4 more
wiley +1 more source
A Novel Coverless Information Hiding Method Based on the Most Significant Bit of the Cover Image
IEEE Access, 2020 With the rapid development of information technology recently, information security has become the focus of public concern. Information Hiding (IH) technology is an effective method to tackle the problem of information leakage incidents. In this paper, a
Lina Yang, Haiyu Deng, Xiaocui Dang
doaj +1 more source
Hiding image into image with hybrid attention mechanism based on GANs
IET Image Processing, Volume 18, Issue 10, Page 2679-2689, 21 August 2024.We introduce a novel network architecture for image steganography with hybrid attention mechanism based on generative adversarial network. Our model consists of three essential components: a generator, an extractor, and a discriminator. Specifically, we have designed a HAM to guide both the current hiding and extraction processes, aiming to improve the
Yuling Zhu +3 more
wiley +1 more source