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SecureVision: Advanced Cybersecurity Deepfake Detection with Big Data Analytics.

Naresh Kumar1, Ankit Kundu2

  • 1Maharaja Surajmal Institute of Technology, New Delhi 110058, India.

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
Summary
This summary is machine-generated.

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SecureVision uses deep learning and big data analytics to detect sophisticated deepfakes in audio and visual media. This advanced system enhances cybersecurity by protecting digital integrity against evolving deceptive techniques.

Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Deepfake technology poses a significant threat to public trust and cybersecurity by enabling sophisticated media manipulation.
  • Existing detection methods struggle against advanced deepfake techniques, necessitating novel approaches.

Purpose of the Study:

  • To introduce SecureVision, an advanced system for detecting deepfake audio and visual content.
  • To enhance the detection of altered media through a novel combination of big data analytics and deep learning.

Main Methods:

  • Utilizing state-of-the-art deep learning algorithms for media analysis.
  • Implementing multi-modal analysis to concurrently process audio and visual data.
  • Leveraging big data analytics for scalability and self-supervised learning for adaptability.
Keywords:
audio analysisbig data analyticscybersecuritydeep learningdeepfake detectiondigital deceptionmanipulated contentmedia integritymulti-modal analysismultimedia analysis

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Main Results:

  • Demonstrated improved deepfake detection capabilities through multi-modal analysis.
  • Showcased the system's efficacy in handling large datasets.
  • Validated the flexibility and robustness of SecureVision against advanced deepfake techniques.

Conclusions:

  • SecureVision offers a proactive, scalable, and efficient defense against deepfake threats.
  • The system establishes a new benchmark for digital integrity, privacy, and security.
  • Multi-modal analysis and self-supervised learning are key to combating evolving digital deception.