# APVCPC: An Adaptive Predicted Value Computation and Pixel Classification Framework for Reversible Data Hiding in Encrypted Images

## TL;DR

This paper introduces a new framework for hiding data in encrypted images that improves data capacity and image quality.

## Contribution

The novel contribution is a context-aware prediction engine that adapts to image texture complexity for better performance.

## Key findings

- APVCPC achieves an average embedding rate exceeding 2.0 bpp.
- The framework ensures perfect reversibility of the original image after data extraction.
- APVCPC outperforms existing methods in both embedding capacity and security.

## Abstract

With the proliferation of Internet of Things (IoT) deployments and mobile sensing systems, reversible data hiding in encrypted images (RDHEI) has emerged as a cornerstone technology for secure cloud-based sensor data management. RDHEI ensures data confidentiality while enabling bit-to-bit restoration of original visual assets. However, conventional RDHEI methods often struggle to optimize the trade-off between high embedding capacity (EC) and the fidelity requirements of sensor-acquired content. This paper proposes an advanced RDHEI framework based on Adaptive Predicted Value Computation and Pixel Classification (APVCPC). The core contribution is a context-aware prediction engine that adaptively selects optimal estimation functions based on local texture complexity, significantly enhancing prediction accuracy in heterogeneous image regions. Subsequently, a content-driven pixel classification paradigm categorizes pixels into loadable (Lpxls) and non-loadable (NLpxls) sets using a dynamic threshold, maximizing the utilization of spatial redundancy. The proposed scheme further supports separable data extraction and image decryption, providing flexible access control for diverse user privileges in secure sensing scenarios. Experimental results on standard benchmarks and the BOW-2 database demonstrate that APVCPC achieves a superior average embedding rate exceeding 2.0 bpp and ensures perfect reversibility, significantly outperforming state-of-the-art techniques in terms of both capacity and security.