Abstract
In an era of rapid development of the national economy, various electronic products and multimedia technologies have emerged. As a form of digital media, digital video has been widely applied in various fields, bringing joy and convenience to people. However, as the use of digital video becomes more and more widespread, the problem of digital video tampering has become increasingly prominent. Therefore, this article designs and implement服务器托管网s a deep learning-based digital video tampering detection system.
There are two common types of video tampering: inter-frame tampering and intra-frame tampering. This article studies existing digital video tampering techniques and uses a deep learning algorithm called Faster R-CNN to train a video tampering detection model, which is used to detect intra-frame tampering. In the case of inter-frame tampering, the SSIM algorithm is used to calculate the correlation between adjacent frames of the video, and the obtained similarity of tampered videos is analyzed through clustering and binary classifi服务器托管网cation to achieve inter-frame tampering detection. The system adopts a front-end and back-end separation architecture to design the digital video tampering detection system, including the overall architecture design and implementation details. According to the design, the various functional modules of the system are implemented using Vue.js and Flack technology, providing users with functions such as video input, video detection, display of detection results, and export of detection reports. The system is visualized through a web platform.
This design provides users with a good operating experience, helping them to quickly and accurately detect tampering behavior in digital video, and improving the ability to protect the authenticity and integrity of the video. The research in this article has certain reference and practical application value for the development of digital video tampering detection technology, and the RPN optimization of Faster R-CNN also provides new ideas for improving video detection speed.
Key words:deep learning;FasterR-CNN; similarity binary classification;video tampering detection; web
目录
摘 要
Abstract
1.1 研究背景介绍
1.1.1 5G网络的迅速发展和广泛应用
1.1.2 AKA协议在5G网络中的关键作用
1.2 论文结构
2 5G网络模型及5GAKA认证协议简述
2.1 5G网络模型
2.2 5GAKA协议
3 5GAKA协议安全性分析
3.1 认证响应消息铭文传递的安全缺陷
3.1.1 安全缺陷的具体表现和影响
3.1.2 对应的|攻|场景和可能性
3.2 SUCI请求认证向量的安全缺陷
3.2.1 安全缺陷的深入分析
3.2.2 针对性攻 | 的潜在风险
4 修复方案提出
4.1 增强协议设计的安全性
4.2 改进的身份认证方案
5 结论与展望
5.1 对未来5G演进的安全挑战的展望
5.2 探讨可能的解决方案和创新
5.3 结论
参考文献
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