基于神经网络的静态图像隐写算法

I. Khan, B. Verma, V. Chaudhari, Ilyas Khan
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引用次数: 11

摘要

隐写技术正在广泛应用于各种不同的数字技术。隐写法将用于互联网/网络安全、水印等。因此,隐写术是将一种通信媒介(文本、声音和图像)隐藏在另一种通信媒介中的过程。它可以工作在JPEG 2000压缩图像和搅拌标记图像。本文提出了一种基于神经网络的隐写分析方法,通过获取图像的统计特征来识别隐藏数据。我们首先提取图像嵌入信息的特征,然后将其输入到神经网络中得到输出。实验结果表明,该方法在“隐写分析”中是有效的,“隐写分析”是检测隐蔽信息的领域。几乎所有的隐写分析都包括手工测试或人工视觉检查,以检测文件是否包含由特定隐写算法隐藏的消息。利用静止图像中的神经网络,通过神经网络算法将数据间接隐藏到图形图像中,获取密码位,然后将生成的密码位放置在载体图像的最低有效位位置,克服了这一障碍。采用异或传播网络模型作为多层感知器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural network based steganography algorithm for still images
Steganographic techniques are being applied across a broad set of different digital technologies. The steganographic method will be used for internet/network security, watermarking and so on. So, the steganography is the process of hiding one medium of communication (Text, Sound, and Image) within another. It can work on JPEG 2000 compressed images & stir Mark images. The new method of steganalysis based on neural network to get the statistics features of images to identify the underlying hidden data. We first extract the features of image embedded information, then input them into neural network to get the output. Experiment result indicates this method is valid in ‘Steganalysis’ The ‘Steganalysis’ is the field of detecting the covert messages. Almost all steganalysis consist of hand-crafted tests or human visual inspection to detect whether a file contains a message hidden by a specific steganography algorithm. The neural network in still images is used to overcome the hurdles by hiding the data indirectly into graphical image using neural network algorithm to get cipher bits, The generated cipher bits are then placed in the least significant bit position of the carrier image. The XOR propagation network model is used which acts as a multilayer perceptron.
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