Systematically Deconstructing APVD Steganography and its Payload with a Unified Deep Learning Paradigm

Md Shamse Tabrej; Kabbo Jit Deb; Md. Azizul Hakim

doi:10.36948/ijfmr.2026.v08i01.70309

Systematically Deconstructing APVD Steganography and its Payload with a Unified Deep Learning Paradigm

Author(s)	Mr. Md Shamse Tabrej, Mr. Kabbo Jit Deb, Mr. Md. Azizul Hakim
Country	India
Abstract	In an era dominated by digital communication, steganography provides a means of covertly embedding data within media files. Adaptive Pixel Value Differencing (APVD) is a sophisticated steganographic technique prized for its high embedding capacity and perceptual invisibility, making it a challenge for traditional steganalysis. This paper addresses the critical need for advanced countermeasures by proposing a deep learning-based approach not only for detecting APVD steganography but also for performing reverse steganalysis—the reconstruction of the hidden payload. We introduce a Convolutional Neural Network (CNN) featuring an attention mechanism and dual output heads for simultaneous stego-detection and payload recovery. Trained and validated on a dataset of 10,000 images from the BOSSbase and UCID repositories, our model achieves a detection accuracy of 96.2%. More significantly, it demonstrates the ability to reconstruct embedded payloads, achieving up to a 93.6% recovery rate at lower embedding densities. The results show a strong inverse correlation between payload size and recovery accuracy. This study highlights a critical vulnerability in adaptive steganographic schemes and provides a powerful new tool for digital forensic investigations, while also prompting a re-evaluation of data security protocols in the face of AI-driven analysis.
Keywords	Steganography, Image Processing, Security. CNN, APDV
Field	Computer > Network / Security
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-02-28
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.70309

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Systematically Deconstructing APVD Steganography and its Payload with a Unified Deep Learning Paradigm

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