Security through data

CONTENT

  • Home
  • Blog
  • Data
  • Directory
  • Events
  • Tutorials

FEATURED

  • CyberAlerts
  • CyberDecoded
  • CyberWeekly
  • CyberStory
  • CyberTips

COMPANY

  • About us
  • Advertise
  • Legal & Policy
Cybermaterial
  • CATEGORIES
    • Alerts
    • APIs
    • Apps
    • Blog
    • Cyber101
    • Documents
    • Entertainment
    • Learning
    • Quotes
    • Stats
    • Tools
No Result
View All Result
Contact Us
Newsletter
Cybermaterial
  • CATEGORIES
    • Alerts
    • APIs
    • Apps
    • Blog
    • Cyber101
    • Documents
    • Entertainment
    • Learning
    • Quotes
    • Stats
    • Tools
No Result
View All Result
Contact Us
Newsletter
Cybermaterial
No Result
View All Result

Deep Fakes and National Security

By Congressional Research Service

in Documents, Papers
1 min read

“Deep fakes”—a term that first emerged in 2017 to describe realistic photo, audio, video, and other forgeries generated with artificial intelligence (AI) technologies—could present a variety of national security challenges in the years to come. As these technologies continue to mature, they could hold significant implications for congressional oversight, U.S. defense authorizations and appropriations, and the regulation of social media platforms.

How Are Deep Fakes Created?

Though definitions vary, deep fakes are most commonly described as forgeries created using techniques in machine learning (ML)—a subfield of AI—especially generative adversarial networks (GANs). In the GAN process, two ML systems called neural networks are trained in competition with each other. The first network, or the generator, is tasked with creating counterfeit data—such as photos, audio recordings, or video footage—that replicate the properties of the original data set. The second network, or the discriminator, is tasked with identifying the counterfeit data. Based on the results of each iteration, the generator network adjusts to create increasingly realistic data. The networks continue to compete—often for thousands or millions of iterations—until the generator improves its performance such that the discriminator can no longer distinguish between real and counterfeit data.

GET REPORT

Tags: DeepfakeDeepfake-documentsDeeptrace
16
VIEWS

Related Papers

Quantum computing 101: Seven questions corporate executives are asking
Documents

Quantum Computing: Lecture Notes

Quantum computing 101: Seven questions corporate executives are asking
Documents

Topological and Subsystem Codes on Low-Degree Graphs with Flag Qubits

Quantum computing 101: Seven questions corporate executives are asking
Documents

Quantum Computing for Computer Scientists

MORE

Books

Book: Hacking: The Art of Exploitation

Hardware

Hak5 USB Rubber Ducky Deluxe Field Guide Book

Stats

56% of Consumers expect their governments should take the primary role for protecting an individual’s data.

I don’t always..

ADVERTISEMENT

Tags

Books Cyber Definition Cybersecurity Hackers Malware Memes Movies Quantum Computing Software Word of the day

© 2021 | CyberMaterial | All rights reserved.

SECURITY THROUGH DATA

No Result
View All Result
  • Home
  • Blog
  • Data
  • Directory
  • Events
  • Tutorials
  • CyberDecoded
  • Stats
  • CyberStory
  • CyberTips
  • Cyber Weekly

© 2020 CyberMaterial - Cyber Decoded.

This website uses cookies. By continuing to use this website you are giving consent to cookies being used. Visit our Privacy and Cookie Policy.