BACK TO ALL BLOGS Flag AI-Generated Text with Hive’s New Classifier HiveFebruary 1, 2023July 29, 2025 Hive is excited to announce our new classifier to differentiate between AI-generated and human-written text. This model is hosted on our website as a free demo, and we encourage users to test out its performance. The recent release of OpenAI’s ChatGPT model has raised questions about how public access to these kinds of large language models will impact the field of education. Certain school districts have already banned access to ChatGPT, and teachers have been adjusting their teaching methods to account for the fact that generative AI has made academic dishonesty a whole lot easier. Since the rise of internet plagiarism, plagiarism detectors have become commonplace at academic institutions. Now a need arises for a new kind of detection: AI-generated text. Our AI-Generated Text Detector outperforms key competitors, including OpenAI itself. We compared our model to their detector, as well as two other popular AI-generated text detection tools: GPTZero and Writer’s AI Content Detector. Our model was the clear frontrunner, not just in terms of balanced accuracy but also in terms of false positive rate — a critical factor when these tools are deployed in an educational setting. Our test dataset consisted of 242 text passages, including ChatGPT-generated text as well as human-written text. To ensure that our model behaves correctly on all genres of content, we included everything from casual writing to more technical and academic writing. We took special care to include texts written by those learning English as a second language, so as to be careful that their writing is not incorrectly categorized by our model due to differences in tone or wording. For these test examples, our balanced accuracy stands at an impressive 99% while the closest competitor is GPTZero with 83%. OpenAI got the lowest of the bunch, with only 73%. Others have tried our model against OpenAI’s in particular, and they have echoed our findings. Following OpenAI’s classifier release, Mark Hachman at PCWorld published an article that suggested that those disappointed with OpenAI’s model should turn to Hive’s instead. In his own informal testing of our model, he praised our results for their accuracy as well as our inclusion of clear confidence scores for every result. A large fear about using these sorts of detector tools in an educational setting is the potentially catastrophic impact of false positives, or cases in which human-written writing is classified as AI-generated. While building our model, we were mindful of the fact that the risk of such high-cost false positives is one that many educators may not want to take. In response, we prioritized lowering our false positive rate. On the test set above, our false positive rate is incredibly low, at 1%. This is compared to OpenAI’s at 12.5%, Writer’s at 46%, and GPTZeros at 30%. Even with our low false positive rate, we do encourage that this tool be used as part of a broader process when investigating academic dishonesty and not as the sole decision maker. Just like plagiarism checkers, it is created to be a helpful screening tool and not a final judge. We are continuously working to improve our model, and any feedback is greatly appreciated. Large language models like ChatGPT are here to stay, and it is crucial to provide educators with tools they can use as they decide how to navigate these changes in their classrooms.
BACK TO ALL BLOGS Spot Deepfakes With Hive’s New Deepfake Detection API HiveNovember 2, 2022July 29, 2025 Contents The Danger of DeepfakesA Look Into Our ModelPutting It All Together: Example Input and ResponseFinal Thoughts The Danger of Deepfakes When generative AI models first gained popularity in the late 2010s, they brought with them the ability to create deepfakes. Deepfakes are synthetic media, typically video, in which one person’s likeness is replaced by another’s using deep learning. They are powerful tools for fraud and misinformation, allowing for the creation of synthetic videos of political leaders and letting scammers easily take on new identities. The primary use, though, of deepfake technology is the fabrication of nonconsensual pornography. The term “deepfake” itself was coined in 2017 by a Reddit user of the same name who made fake pornographic videos featuring popular female celebrities. In 2019, the company Sensity AI catalogued deepfakes across the web and reported that a whopping 96% of them were pornographic, all of which were of women. In the years since, more of this sort of deepfake pornography has become readily available online, with countless forums and even entire porn sites dedicated to it. The targets of this are not just celebrities. They are also everyday women superimposed into adult content by request—on-demand revenge porn for anyone with an internet connection. Many sites have banned deepfakes entirely, since they are far more often used for harm than for good. At Hive, we’re committed to providing API-accessible solutions for challenging moderation problems like this one. We’ve built our new Deepfake Detection API to empower enterprise customers to easily identify and moderate deepfake content hosted on their platforms. This blog post explains how our model identifies deepfakes and the new API that makes this functionality accessible. A Look Into Our Model Hive’s Deepfake Detection model is essentially a version of our Demographic API that is optimized to identify deepfakes as opposed to demographic attributes. When a query is submitted, this visual detection model locates any faces present in the input. It then performs an additional classification step that determines whether or not each detected face is a deepfake. In its response, it provides a bounding-box location and classification (with confidence scores) for each face. While the face detection aspect of this process is the same as the one used for our industry-leading Demographic API, the classification step was fine-tuned for deepfake identification by training on a vast repository of synthetic and real video data. Many of these examples were pulled from genres commonly associated with deepfakes, such as pornography, celebrity interviews, and movie clips. We also included other types of examples in order to create a classifier that identifies deepfakes across many different content genres. Putting It All Together: Example Input and Response With only one head, the response of our Deepfake Detection model is easily interpretable. When an image or video query is submitted, it is first split into frames. Each frame is then analyzed by our visual detection model in order to find any faces present in the image. Every face then receives a deepfake classification — either yes_deepfake or no_deepfake. Confidence scores for these classifications range from 0.0 to 1.0, with a higher score indicating higher confidence in the model’s results. Example Deepfake Detection input and API response Here we see the deepfaked image and, to its left, the two original images used to create it. This input image doesn’t appear to be fake at first glance, especially when the image is displayed at a small size. Even with a close examination, a human reviewer could fail to realize that it is actually a deepfake. As the example illustrates, the model correctly identifies this realistic deepfake with a high confidence score of more than 0.99. Since there is only one face present in this image, we see one corresponding “bounding poly” in the response. This “bounding poly” contains all model response information for that face. Vertices and dimensions are also provided, though those fields are truncated here for clarity. Because deepfakes like this one can be very convincing, they are difficult to moderate with manual flagging alone. Automating this task is not only ideal to accelerate moderation processes, but also to spot realistic deepfakes that human reviewers might miss. Digital platforms, particularly those that host NSFW media, can integrate this Deepfake Detection API into their workflows by automatically screening all content as it is posted. Video communication platforms and applications that use any kind of visual identity verification can also utilize our model to counter deepfake fraud. Final Thoughts Hive’s Deepfake Detection API joins our recently released AI-Generated Media Recognition API in the aim to expand content-moderation to keep up with the fast-growing domain of generative AI. Moving forward, we plan to continually update both models so as to best keep up with new generative techniques, popular content genres, and emerging customer needs. The recent popularity of diffusion models like Stable Diffusion, Midjourney, and DALL-E 2 has brought deepfakes back into the spotlight and sparked conversation on whether these newer generative techniques can be used to develop brand-new ways of making them. Whether or not this happens, deepfakes aren’t going away any time soon and are only growing in number, popularity, and quality. Identifying and removing them across online platforms is crucial to limit the fraud, misinformation, and digital sexual abuse that they enable. If you’d like to learn more about our Deepfake Detection API and other solutions we’re building, please feel free to reach out to sales@thehive.ai or contact us here.
BACK TO ALL BLOGS Detect and Moderate AI-Generated Artwork Using Hive’s New API HiveSeptember 23, 2022July 29, 2025 Try Our Demo To try our AI-Generated Image Detection model out for yourself, check out our demo. Contents A New Need for Content ModerationUsing AI to Identify AI: Building Our ClassifierHow it Works: An Example Input and ResponseFinal Thoughts and Future Directions A New Need for Content Moderation In the past few months, AI-generated art has experienced rapid growth in both popularity and accessibility. Engines like DALL-E, Midjourney, and Stable Diffusion have spurred an influx of AI-generated artworks across online platforms, prompting an intense debate around their legality, artistic value, and potential for enabling the propagation of deepfake-like content. As a result, certain digital platforms such as Getty Images, InkBlot Art, Fur Affinity, and Newgrounds have announced bans on AI-generated content entirely, with more to likely follow in the coming weeks and months. Platforms are enacting these bans for a variety of reasons. Online communities built for artists to share their artwork such as Newgrounds, Fur Affinity, and Purpleport stated they put their AI artwork ban in place in order to keep their sites focused exclusively on human-created art. Other platforms have taken action against AI-generated artwork due to copyright concerns. Image synthesis models often include copyrighted images in their training data, which consist of massive amounts of photos and artwork scraped from across the web, typically without any artists’ consent. It is an open question whether this type of scraping and the resulting AI-generated artwork amount to copyright violations — particularly in the case of commercial use — and platforms like Getty and InkBlot Art don’t want to take that risk. As part of Hive’s commitment to providing enterprise customers with API-accessible solutions to moderation problems, we have created a classification model made specifically to assist digital platforms in enacting these bans. Our AI-Generated Media Recognition API is built with the same type of robust classification model as our industry-leading visual moderation products, and it enables enterprise customers to moderate AI-generated artwork without relying on users to flag images manually. This post explains how our model works and the new API that makes this functionality accessible. Using AI to Identify AI: Building Our Classifier Hive’s AI-Generated Media Recognition model is optimized for use with the kind of media generated by popular AI generative engines such as DALL-E, Midjourney, and Stable Diffusion. It was trained on a large dataset comprising millions of artificially generated images and human-created images such as photographs, digital and traditional art, and memes sourced from across the web. The resulting model is able to identify AI-created images among many different types and styles of artwork, even correctly identifying AI artwork that could be misidentified by manual flagging. Our model returns not only whether or not a given image is AI-generated, but also the likely source engine it was generated from. Each classification is accompanied by a confidence score that ranges from 0.0 to 1.0, allowing customers to set a confidence threshold to guide their moderation. How it Works: An Example Input and Response When receiving an input image, our AI-Generated Media Recognition model returns classifications under two separate heads. The first provides a binary classification as to whether or not the image is AI-generated. The second, which is only relevant when the image is classified as an AI-made image, identifies the source of that artificial image from among the most popular generation engines that are currently in use. To get a sense of the capabilities of our AI-Generated Media Recognition model, here’s a look at an example classification: This input image was created with the AI model Midjourney, though it is so realistic that it may be missed by manual flagging. As shown in the response above, our model correctly classifies this image as AI-generated with a high confidence score of 0.968. The model also correctly identifies the source of the image, with a similarly high confidence score. Other sources like DALL-E are also returned along with their respective confidence scores, and the scores under each of the two model heads sum to 1. Platforms that host artwork of any kind can integrate this AI-Generated Media Recognition API into their workflows by automatically screening all content as it is being posted. This method of moderating AI artwork works far more quickly than manual flagging and can catch realistic artificial artworks that even human reviewers might miss. Final Thoughts and Future Directions Digital platforms are now being flooded with AI-generated content, and that influx will only increase as these generative models continue to grow and spread. On top of this, creating this kind of artwork is fast and easy to access online, which enables large quantities of it to be produced quickly. Moderating artificially created artworks is crucial for many sites to maintain their platform’s mission and protect themselves and their customers from potential legal issues further down the line. We created our AI-Generated Media Recognition API to solve this problem, but our model will need to continue to evolve along with image generation models as existing ones improve and new ones are released. We plan on adding new generative engines to our sources as well as continually updating our model to keep up with the current capabilities of these models. Since some newer generative models can create video in addition to still images, we are working to add support for video formats within our API in order to best prevent all types of AI-generated artwork from dominating online communities where they are unwelcome. If you’d like to learn more about this and other solutions we’re building, please feel free to reach out to sales@thehive.ai or contact us here.