Performance Evaluation And Deep Learning Steganalysis Of Covert APK Transmission Using Video Steganography Using 2D Haar Discrete Wavelet Transform And LSB Substitution

Authors

  • Nilima Dongre Ramrao Adik Institute of Technology, D Y Patil deemed to be University
  • Ponmalar G Ramdeobaba university
  • Pavan Prajapati Ramrao Adik Institute of Technology,D Y Patil deemed to be University
  • Nityam Padwal Ramrao Adik Institute of Technology,D Y Patil deemed to be University
  • Niraj Karande Ramrao Adik Institute of Technology,D Y Patil deemed to be University

DOI:

https://doi.org/10.70917/ijcisim-2026-2285

Keywords:

video steganography, APK binary embedding, 2D Haar DWT, LSB substitution, Hamming (7,4) error correction, XOR encryption, Steganalysis, VGG16 fine-tuning, CNN-LSTM, NPCR, UACI, Shannon entropy, Android security.

Abstract

The hidden payload recovery should be exact rather than approximated as there is zero tolerance for byte corruption in APK files. WE aim to develop a system which ensure zero-byte-error recovery for the APK while keeping the stego-video visually indistinguishable from a normal video. We have developed a complete pipeline for covert Android Package file transmission through video steganography, addressing the one engineering requirement that distinguishes APK embedding from every prior payload class in the field: Android's PackageManager rejects any recovered binary containing a single incorrect byte, making zero-error recovery a hard constraint rather than a quality target. The research demonstrates that Hamming (7,4) forward error correction at code rate 4/7 successfully satisfies this constraint across the embedding-extraction cycle, protecting the APK byte stream before LH sub-band coefficient modification and reconstructing it without residual errors under lossless storage conditions. The implementation of 2D Haar Discrete Wavelet Transform embedding in the horizontal-detail LH sub-band proved effective at maintaining imperceptibility despite the elevated payload densities that APK file sizes require, achieving PSNR of 47.3 to 52.1 dB, SSIM above 0.99, NPCR of 89.66%, and UACI of 0.82% within the accepted imperceptibility range. The implementation of VGG16 fine-tuning for spatial LH artefact detection proved effective at classifying stego frames with 91.52% accuracy and F1-score of 91.42%, successfully distinguishing embedded from clean frames despite the absence of prior training data for this specific payload class. The results have validated the effectiveness of frequency-domain binary payload embedding and highlight the potential for neural steganalysis as a practical detection mechanism for APK-in-video covert channels. The integration of all three pipelines into a single deployable desktop GUI application represents an advance not present in any prior covert APK transmission system.

Downloads

Download data is not yet available.

Downloads

Published

2026-06-23

How to Cite

Nilima Dongre, Ponmalar G, Pavan Prajapati, Nityam Padwal, & Niraj Karande. (2026). Performance Evaluation And Deep Learning Steganalysis Of Covert APK Transmission Using Video Steganography Using 2D Haar Discrete Wavelet Transform And LSB Substitution. International Journal of Computer Information Systems and Industrial Management Applications, 18(3s), 142–161. https://doi.org/10.70917/ijcisim-2026-2285

Issue

Section

Original Articles