The Tactics of Deepfake Creators - Deepstash
The Tactics of Deepfake Creators

The Tactics of Deepfake Creators

Curated from: trajectorymatrix.substack.com

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What is Deepfake?

What is Deepfake?

Deepfake is an advanced digital manipulation technique that uses artificial intelligence algorithms, specifically machine learning and neural networks, to create realistic-looking fake videos or images (synthetic media). These manipulated media can convincingly depict people doing or saying things that never actually occurred in reality.

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Deepfake Generation

Deepfake Generation

Deepfake creators are continually refining their techniques to evade detection. They leverage advancements in AI, particularly in machine learning and neural networks, to improve the realism of deepfakes. As detection methods become more sophisticated, so will the generation techniques, resulting in a perpetual cat-and-mouse game.

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Key Data

Key Data

  • Researchers predict that by 2026, as much as 90% of online content could be synthetically generated.
  • In 2024, disinformation has been identified as a leading global threat, with the use of deepfakes standing out as one of the most concerning applications of AI.
  • The amount of deepfake content online surged by 900% from 2019 to 2020.
  • The deepfake AI market is experiencing significant growth, with forecasts predicting a jump from USD 7.0 billion in 2024 to USD 38.5 billion by 2030, driven by a strong CAGR of 33.5%.

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The Detection Methods Used by Experts to Identify Deepfake

The Detection Methods Used by Experts to Identify Deepfake

1. Deep Learning Algorithms

2. Forensic Analysis

3. Digital Watermarking

4. Blockchain Technology

5. Biometric Comparison

6. Audio Analysis

7. Liveness Detection

8. Multifactor Authentication

9. Sentinel & FakeCatcher

10. WeVerify & Microsoft’s Video Authenticator Tool

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How do Deepfake Creators Evade the Anti-Fraud Measures?

How do Deepfake Creators Evade the Anti-Fraud Measures?

Improving Resolution, Better Face-Swapping Techniques, Refining Audio, GAN Refinement, and Adaptive Learning are some of the tactics they follow to dodge the detection methods.

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