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SecureVision:通过大数据分析实现先进的网络安全深度伪造检测。

SecureVision: Advanced Cybersecurity Deepfake Detection with Big Data Analytics.

作者信息

Kumar Naresh, Kundu Ankit

机构信息

Maharaja Surajmal Institute of Technology, New Delhi 110058, India.

New York Institute of Technology, Vancouver, BC V5M 4X5, Canada.

出版信息

Sensors (Basel). 2024 Sep 29;24(19):6300. doi: 10.3390/s24196300.

Abstract

SecureVision is an advanced and trustworthy deepfake detection system created to tackle the growing threat of 'deepfake' movies that tamper with media, undermine public trust, and jeopardize cybersecurity. We present a novel approach that combines big data analytics with state-of-the-art deep learning algorithms to detect altered information in both audio and visual domains. One of SecureVision's primary innovations is the use of multi-modal analysis, which improves detection capabilities by concurrently analyzing many media forms and strengthening resistance against advanced deepfake techniques. The system's efficacy is further enhanced by its capacity to manage large datasets and integrate self-supervised learning, which guarantees its flexibility in the ever-changing field of digital deception. In the end, this study helps to protect digital integrity by providing a proactive, scalable, and efficient defense against the ubiquitous threat of deepfakes, thereby establishing a new benchmark for privacy and security measures in the digital era.

摘要

SecureVision是一个先进且值得信赖的深度伪造检测系统,旨在应对日益增长的“深度伪造”视频威胁,这些视频会篡改媒体内容、破坏公众信任并危及网络安全。我们提出了一种新颖的方法,将大数据分析与最先进的深度学习算法相结合,以检测音频和视频领域中的篡改信息。SecureVision的主要创新之一是使用多模态分析,通过同时分析多种媒体形式并增强对先进深度伪造技术的抵抗力来提高检测能力。该系统管理大型数据集和集成自监督学习的能力进一步增强了其功效,这保证了它在不断变化的数字欺骗领域中的灵活性。最后,本研究通过提供针对无处不在的深度伪造威胁的主动、可扩展且高效的防御措施,有助于保护数字完整性,从而为数字时代的隐私和安全措施树立了新的标杆。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd62/11478486/3f1305f46609/sensors-24-06300-g001.jpg

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