Difference between revisions of "Talk:Deepfake Detectors"
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Deepfakes are audio or video fabrication or manipulation that are synthetically created or generated using deep learning algorithms. Deepfake content is created by using two competing AI algorithms — one is called the generator and the other is called the discriminator. The generator, which creates the phony multimedia content, asks the discriminator to determine whether the content is real or artificial. Together, the generator and discriminator form something called a generative adversarial network. Each time the discriminator accurately identifies content as being fabricated, it provides the generator with valuable information about how to improve the next deepfake. | Deepfakes are audio or video fabrication or manipulation that are synthetically created or generated using deep learning algorithms. Deepfake content is created by using two competing AI algorithms — one is called the generator and the other is called the discriminator. The generator, which creates the phony multimedia content, asks the discriminator to determine whether the content is real or artificial. Together, the generator and discriminator form something called a generative adversarial network. Each time the discriminator accurately identifies content as being fabricated, it provides the generator with valuable information about how to improve the next deepfake. | ||
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| + | 4. Added a screenshot of a tweet by former president Donald Trump to illustrate the usage of deepfakes to spread misinformation. | ||
| + | 5. Added minor edits and corrections to improve readability, grammar and coherence throughout the wiki. | ||
Revision as of 12:59, 19 March 2021
Collaborative Edits: Soumya Tejam
1. Added a new section titled "Ethical Implications of Deepfakes in Democratic Processes" which builds upon the mention of deepfakes used during election in the introduction. I've explored the ethical implications of a specific subsection of this vast topic, by focusing on how deepfakes impact the integrity of the electoral vote. (added 280 words)
2. Added four new references, specifically related to deepfakes in the democratic process:
- Hasen RL (2019) Deep Fakes, Bots, and Siloed Justices: American Election Law in a Post-Truth World. St. Louis University Law Review.
- Marshall WP (2004) False Campaign Speech and the First Amendment. U. Pennsylvania Law Review 153.
- Daniels GR (2009) Voter Deception. Indiana Law Review 43.
- Chesney R and Citron DK (2019) Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security. California Law Review 107.
3. Edited 'Introduction Section' to include a more thorough definition and understanding of deepfakes and the underlying technology.
Deepfakes are audio or video fabrication or manipulation that are synthetically created or generated using deep learning algorithms. Deepfake content is created by using two competing AI algorithms — one is called the generator and the other is called the discriminator. The generator, which creates the phony multimedia content, asks the discriminator to determine whether the content is real or artificial. Together, the generator and discriminator form something called a generative adversarial network. Each time the discriminator accurately identifies content as being fabricated, it provides the generator with valuable information about how to improve the next deepfake.
4. Added a screenshot of a tweet by former president Donald Trump to illustrate the usage of deepfakes to spread misinformation. 5. Added minor edits and corrections to improve readability, grammar and coherence throughout the wiki.
