Determining What’s Real: Reality vs. Deepfakes

Deepfakes: Questioning reality through the screens.
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Morgann

Artificial intelligence has come a long way in recent years, and it is now possible for AI-generated images and videos to deceive even the most discerning viewer. Unfortunately, the digital tools available to detect such deepfakes are still struggling to keep up with the pace of content generation, likely because there is little financial incentive for companies to invest in this area.

Deepfakes, which refer to manipulated images and videos created using AI technology, have been a cause for concern for several years now. While some deepfakes may seem harmless, such as the recent image of Pope Francis wearing a Balenciaga coat, others have the potential to cause serious damage. For example, deepfakes can be used to manipulate elections or to create false allegations of sexual misconduct.

Despite the potential risks associated with deepfakes, the global market for technology to root out manipulated content is relatively small. According to research firm HSRC, the global market for deepfake detection was valued at $3.86 billion in 2020 and is expected to expand at a compound annual growth rate of 42% through 2026.

Experts agree that there is too much focus on AI generation and not enough on detection. Claire Leibowicz, head of the AI and Media Integrity Program at the nonprofit organization The Partnership on AI, has stated that there is undue attention on AI generation and not enough on detection.

While many big tech companies have poured billions of dollars into generative AI, there is little legal or financial incentive to invest in deepfake detection. Jeff Pollard, an analyst at Forrester Research, has stated that while security leaders are concerned about generative AI, they do not typically spend budget on deepfake detection. This is because they have so many other problems to deal with.

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Despite the lack of financial incentives, some startups and big tech companies are developing deepfake detection technology. Intel Corp. launched its FakeCatcher product last November as part of its work in responsible AI. The technology looks for authentic clues in real videos by assessing human traits such as blood flow in the pixels of a video and can detect fakes with 96% accuracy, according to the company.

Netherlands-based Sensity AI and Estonia-based Sentinel are also developing deepfake detection technology, mainly serving governments and businesses that want to reduce fraud. However, current costs for such services can range from tens of thousands of dollars to millions, as they require expensive graphics processing chips and cloud computing power. Platforms like Facebook and Twitter are not required by law to detect and alert deepfake content on their platforms, leaving consumers in the dark.

Ben Colman, CEO of Reality Defender, has stated that “the only organizations that do anything are the ones like banks that have a direct connection to financial fraud.”

Current methods of detecting fake images and videos involve comparing visual characteristics in the content by training computers to learn from examples and embedding watermarks and camera fingerprints on original works. However, the rapid proliferation of deepfakes requires more powerful algorithms and computing resources.

As the use of deepfakes becomes more widespread, it is crucial to develop reliable and effective detection methods. Without a commercially available and widely adopted tool to distinguish fake online content from real, there is plenty of opportunity for bad actors to use deepfakes for their own purposes.

The development of deepfake detection technology is not just about making money; it is also about reducing online disinformation and protecting individuals from harm. As we move forward, it is essential to invest in this area and prioritize the development of more advanced deepfake detection tools.

 

Questioning reality through the screens.

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