BACK TO ALL BLOGS Build Your Own Custom ML Models with Hive AutoML HiveMay 3, 2023March 5, 2025 We’re excited to announce Hive’s new AutoML tool that provides customers with everything they need to train, evaluate, and deploy customized machine learning models. Our pre-trained models solve a wide range of use cases, but we will always be bounded by the number of models we can build. Now customers who find that their unique needs and moderation guidelines don’t quite match with any of our existing solutions can create their own, custom-built for their platform and easily accessible via API. AutoML can be used to augment our current offerings or to create new models entirely. Want to flag a particular subject that doesn’t exist as a head in our Text Moderation API, or a certain symbol or action that isn’t part of our Visual Moderation? With AutoML, you can quickly build solutions for these problems that are already integrated with your Hive workflow. Let’s walk through our AutoML process to illustrate how it works. In this example, we’ll build a text classification model that can determine whether or not a given news headline is satirical. First, we need to get our data in the proper format. For text classification models, all dataset files must be in CSV format. One column should contain the text data (titled text_data) and all other columns represent model heads (classification categories). The values within each row of any given column represent the classes (possible classifications) within that head. An example of this formatting for our satire model is shown below: The first page you’ll see on Hive’s AutoML platform is a dashboard with all of your organization’s training projects. In the image below, you’ll see how the training and deployment status of old projects are displayed. To create our satire classifier, we’re going to make a new project by hitting the “Create New Project” button in the top right corner. We’ll then be prompted to provide a name and description for the project as well as training data in the form of a CSV file. For test data, you can either upload a separate CSV file or choose to randomly split your training data into two files, one to be used for training and the other for testing. If you decide to split your data, you will be able to choose the percentage that you would like to split off. After all of that is entered, we are ready to train! Beginning model training is as easy as hitting a single button. While your model trains, you can easily view its training status on the Training Projects page. Once training is completed, your project page will show an analysis of the model’s performance. The boxes at the top allow you to decide if you want to look at this analysis for a particular class or overall. If you’re building a multi-headed model, you can choose which head you’d like to evaluate as well. We provide precision, recall, and balanced accuracy for all confidence thresholds as well as a PR curve. We also display a confusion matrix to show how many predictions were correct and incorrect per class. Once you’re satisfied with your model’s performance, select the “Create Deployment” to launch the model. Similarly to model training, deployment will take a few moments. After model deployment is complete, you can view the deployment in your Hive customer dashboard, where you can access your API key, view current tasks, as well as access other information as you would with our pre-trained models. We’re very excited to be adding AutoML to our offerings. The platform currently supports both text and image classification, and we’re working to add support for large language models next. If you’d like to learn more about our AutoML platform and other solutions we’re building, please feel free to reach out to sales@thehive.ai or contact us here.
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.