隐写分析技术
计算机科学
隐写术
位图
隐写工具
人工智能
图像(数学)
卷积神经网络
信息隐藏
深度学习
机器学习
模式识别(心理学)
数据挖掘
作者
Alejandro Martín,Alfonso Hernández,Moutaz Alazab,Jason J. Jung,David Camacho
标识
DOI:10.1016/j.eswa.2023.119841
摘要
Images have been repeatedly used as the perfect environment to hide information through the use of steganography techniques. Whether messages, documents or even other images, the bitmap of an digital picture provides a place where hidden data can be embedded without human notice. So far, a plethora of steganography methods can be found in the state-of-the-art literature, together with steganalysis techniques, devoted to detect the presence of hidden information in files. Recent steganography techniques rely on Convolutional Neural Networks, trying to embed as information as possible while minimising visual changes in the image. Following this trend, this article tries to demonstrate that a Generative Adversarial Network (GAN) can be used to improve the ability of a spatial domain steganalysis method and to insert secret information with minimal image alteration. Through a training process, the GAN learns how to adapt an image to later introduce a message using the Least Significant Bit steganography algorithm. The results evidence that the approach is successful at avoiding detection by a state-of-the-art Deep Learning steganalysis architecture.
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