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Nuanced Blog

Love in the Age of Deepfakes: Protecting Dating App Users' Hearts and Identities

There has been an alarming surge in AI-generated deepfakes, transforming the way fraudsters perpetrate romance scams. Dating apps are increasingly becoming breeding grounds for deception, as scammers harness AI to create convincing, yet entirely fake, dating profiles. This phenomenon is reshaping online dating, and it's imperative for dating platforms to fortify their defenses.

The Dangerous Implications of Deepfakes in Explicit Content

Deepfakes have been used to create explicit content without the consent of the individuals depicted. High-profile celebrities like Taylor Swift and Megan Thee Stallion have recently been targeted by such malicious uses of deepfakes, highlighting the urgent need for regulatory action and technological solutions to combat this growing issue.

Nuanced is tackling this issue head-on with our new deepfake detection model.

Nuanced Recognized as a Key Solution in FTC Regulation on Deepfakes

We are thrilled to announce that DragonFly Capital has highlighted Nuanced as a proposed solution to combat AI impersonations in their comment letter to the Federal Trade Commission (FTC). This recognition underscores the effectiveness and importance of our deepfake and AI fraud detection technology in protecting consumers and promoting responsible AI growth.

Explainable AI for Transparency and Trust

Teams frequently face “build or buy” decisions when evaluating the cost-to-benefit ratio of using external vendors versus investing in building something in-house. In our previous experience, one frequent consideration that would come up is whether the onboarding and maintenance of a new service would cost more time and effort than not having integrated it at all.

This was a particularly sensitive matter when it came to software services that generated recommendations for fraud or abuse detection using analytics or predictive AI. As expected, these systems sometimes produced incorrect results. While it is expected that no system is perfect, one consistent source of frustration encountered by our teams was the lack of transparency around how a given recommendation was made by a selected tool, costing engineers, data scientists, and analysts countless hours spent investigating black box decisions.

For this reason, we chose to make Nuanced a product that not only yields an overall evaluation of whether a given image is likely generated by AI, but also provide some level of interpretability for said decision.

The Glossily Rendered Elephant in the Room or: Why We are Building Our Own Models

With the accelerating rate of advancement in AI and its seeming integration into all things, the decision for many companies is whether or not to use in-house models to provide these services. At Nuanced, we aim to balance innovation, customer satisfaction, privacy and pricing. Providing our service to detect and identify AI-generated content, we chose to develop and run our models ourselves, which we believe will uphold our aforementioned commitments. There are a myriad of reasons as to why we made this decision, and we believe, moving forward, more and more companies may plan to do so themselves.

How Generative AI has transformed the online spam and abuse landscape

The emergence of generative AI has given rise to an alarming increase in AI-generated spam and abuse.

Products like ChatGPT, DALL-E, and GitHub Copilot have showcased remarkable content creation capabilities. Models underlying such technologies mirror their training material to craft content ranging from text, images, music, and code, often producing outcomes that can enhance many aspects of our lives. However, this very capability also opens the door to misuse, particularly in the generation of spam and abusive material.

Should platforms be required to identify and flag AI-generated content?

One frequently debated question is whether platforms should be required to identify and flag generative AI content.

Like many such questions, the answer to this one depends entirely on context. Since the identification of generative AI content is only useful insofar as it mitigates harm, a more pertinent question to ask is: what new risks and potential for harm does generative AI content create? It is then worth asking: how can those risks be mitigated?