As election day unfolds, the concern around deepfakes and AI-generated images centers around how they might manipulate public opinion. While it's true that AI-generated images and videos can mislead voters by showing candidates doing or saying things they never did, there's a subtler yet equally urgent risk that isn’t grabbing headlines: AI-driven misinformation that confuses voters on the logistics of voting itself.
The World Economic Forum has ranked misinformation as the top global risk over the next two years, even above extreme weather events and war.
In the midst of this and other such concerns, watermarking has emerged as a popular technique to label and identify AI-generated content. Companies like Meta and OpenAI have adopted visible markers and invisible watermarks to indicate AI-generated images, audio, and video. While these efforts represent a step in the right direction, watermarking alone is insufficient. This blog post explores the limitations of watermarking and why robust AI-generated image detection technology remains essential.
Identity verification and KYC systems are facing a growing threat from AI-generated images. Traditionally, these systems rely on image-based verification, requiring users to submit selfies and photographs of government-issued IDs or other personal documents. While these methods have been effective for years, AI-generated images are now being used to circumvent these safeguards, making it increasingly difficult to distinguish between genuine and fraudulent submissions.
Exciting news! Last week, we put our deepfake detection technology to the test against the newly released SD3, and the results are in!
AI-generated images are increasingly used to create fake IDs, such as driver's licenses and passports, bypassing traditional ID verification checks and facilitating fraud and 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.
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.
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.
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.
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.