In the era of digital communication, steganography allows covert embedding of data within media files. Adaptive Pixel Value Differencing (APVD) is a steganographic method valued for its high embedding capacity and invisibility, posing challenges for traditional steganalysis. This paper proposes a deep learning-based approach for detecting APVD steganography and performing reverse steganalysis, which reconstructs the hidden payload. We present a Convolutional Neural Network (CNN) with an attention mechanism and two output heads for simultaneous stego detection and payload recovery. Trained and validated on 10,000 images from the BOSSbase and UCID datasets, our model achieves a detection accuracy of 96.2 percent. It also reconstructs embedded payloads with up to 93.6 percent recovery at lower embedding densities. Results indicate a strong inverse relationship between payload size and recovery accuracy. This study reveals a vulnerability in adaptive steganography and provides a tool for digital forensic analysis, while encouraging reassessment of data security in the age of AI-driven techniques.

翻译：在数字通信时代，隐写术允许将数据隐蔽地嵌入媒体文件中。自适应像素值差分（APVD）是一种因其高嵌入容量和不可见性而受到重视的隐写方法，对传统隐写分析构成了挑战。本文提出了一种基于深度学习的方法，用于检测APVD隐写术并执行反向隐写分析，从而重建隐藏载荷。我们提出了一种带有注意力机制和两个输出头的卷积神经网络（CNN），用于同时进行隐写检测和载荷恢复。在来自BOSSbase和UCID数据集的10,000张图像上进行训练和验证后，我们的模型实现了96.2%的检测准确率。在较低嵌入密度下，它还能以高达93.6%的恢复率重建嵌入载荷。结果表明，载荷大小与恢复准确率之间存在强烈的反比关系。本研究揭示了自适应隐写术中的一个漏洞，为数字取证分析提供了工具，同时鼓励在人工智能驱动技术时代重新评估数据安全性。