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Updated: Jun 5, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

使用自主监督的卷积神经网络对图像隐形图像进行杀菌.

Jinjin Liu¹, Fuyong Xu², Yingao Zhao²

¹Software College of Software, Henan Normal University, Xinxiang, Henan, China.

PeerJ. Computer science

|December 9, 2024

概括

这项研究介绍了一种自我监督的卷积神经网络 (SS-Net),以有效地从未知图像中删除隐藏的消息,增强网络安全. 通过SS-Net实现了100%的stego图像屏蔽率,同时保持了图像质量.

关键词:

图像阶段分析分析自主监督学习学习史蒂冈图形 (Steganography) 是一种隐藏的图形.隐藏术灭菌灭菌隐藏术灭菌

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科学领域:

计算机科学计算机科学
网络安全网络安全
人工智能的人工智能

背景情况:

隐蔽图像可以实现隐蔽通信,增加网络安全风险.
目前的隐形图谱灭菌方法需要已知的封面-步骤图像对,限制对未知图像的有效性.
现有的技术很难在社交网络上有效地阻止stego图像.

研究的目的:

提出一个有效的图像隐形图像灭菌方法.
为了解决在清理未知图像方面现有方法的局限性.
为了提高stego图像在社交网络中的屏蔽率.

主要方法:

提出了一个自我监督的卷积神经网络 (SS-Net),消除了对先前隐形图学知识的需求.
SS-Net使用了一个净化模块,用于自主监督学习的像素混合下方采样.
一个精细化模块,结合中央掩盖的卷积和扩展的卷积余块,消除秘密消息,并保持图像质量.

主要成果:

SS-Net 在 BOSSbase 测试集上实现了 100% 的阻断率.
该方法有效地平衡了秘密消息的破坏与图像质量的保存.
在消息删除和图像质量保留方面,SS-Net的性能优于最先进的方法.

结论:

拟议的SS-Net方法为消毒未知stego图像提供了有效的解决方案.
这种方法通过阻止秘密通信,显著提高了网络安全.
与现有方法相比,SS-Net在消息删除和图像质量保护方面都提供了卓越的性能.