AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The proposed DPVD–LBP–LSB framework provides an effective and practical approach for high capacity image steganography, with comprehensive evaluation against advanced steganalysis methods remaining for future work.
Secure exchange of confidential data over open communication networks requires image data-hiding methods that preserve imperceptibility while providing sufficient payload and key-controlled access. This paper presents a Kirsch Edge Detector based Variable Data Hiding (KED-VDH) technique for spatial-domain image stegano...
The purpose of image steganography, a common information-hiding method, is to embed hidden data into digital images while retaining their visual quality and preventing statistical detection. Although Discrete Wavelet Transform (DWT)-based methods improve imperceptibility by using the frequency-domain characteristics, t...
Image steganography is widely used to hide information by embedding secret data within digital images while preserving their visual quality. Among various techniques, Quantization Index Modulation (QIM) provides reliable data extraction; however, conventional fixed QIM methods often introduce unnecessary distortion, as...
This study proposes a methodology based on Discrete Cosine Transform (DCT) coefficients and deep learning to identify steganographic algorithms (UERD, J-UNIWARD, and JMiPOD) and to classify distortion levels in images from the ALASKA2 dataset. For each image, 258,048 AC coefficients were extracted, and 9999 were select...
2D Gaussian-based image representation is becoming increasingly popular, and our work proposes a new approach to steganography by embedding information within Gaussian parameters rather than image pixels. We first fit the parameters of this representation to the target image and employ a secret key to select a subset o...
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