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.
BACK TO ALL BLOGS Mensio Product Update HiveJuly 28, 2022July 4, 2024 Mensio aims to provide users with the most comprehensive and granular data available in the industry to inform better decisions on how to optimize the investment of marketing dollars. In recent months, brands, agencies, and rights holders alike have expressed interest in being able to a) measure the presence and value of verbal mentions within programming alongside visual exposures and b) within sports, understand the relative contributions of different sponsorship assets to total exposure and its associated value. We are excited to announce a set of major product upgrades to incorporate these requests now live within Mensio’s Sponsorship & Branded Content modules. While we have supported these capabilities “off-platform” using Hive’s Logo Location and Brand Mentions models for multiple years, the inclusion of these capabilities in-platform provide reduced friction, faster access to data, and richer levels of brand- and property-level analysis as well as competitive intelligence. Below is a brief summary of what’s new; as with all releases, notes will also appear as a pop-up in-platform upon your next log-in. Your Hive point of contact will additionally introduce the new capabilities live in your next scheduled meeting and, if not imminent, will be reaching out to schedule time for an overview of the new features at your earliest convenience. New Features Now Available: Reporting by Asset Type For Televised Sports Programming Sponsorship & Branded Content modules now include reporting of exposures by asset type across most televised sports programming. Reporting includes 25+ standard asset types including jerseys, TVGI / digital overlays, lower level banners (i.e., outfield wall, dasherboards, courtside LED, etc.), basket stanchions, and more. Within the platform, asset types are integrated as filters into existing reporting of visual exposures by brand, by program, and by occurrence. Additionally, two new pages have been added featuring asset-centric views of exposure by brand and by program. Data is currently available for all relevant programming since June 1, 2022, and will be available going back to September 1, 2022 shortly. Notifications will appear within the platform as additional historical information becomes available. Now Available: Reporting of Verbal Mentions Across Television Programming Sponsorship & Branded Content television modules now include reporting of verbal mentions across all television programming. Verbal mentions are integrated into summary metrics in the Competitive Insights section, and have dedicated pages for deep dives by brand, by program, and by occurrence. Data is currently available for all relevant programming since June 1, 2021, and will be available going back to October 1, 2018 shortly. Notifications will appear within the platform as additional historical information becomes available. Now Available: Updated Module Definition and Navigation To accommodate the expanded data, we have reconfigured module contents and navigation for Sponsorship & Branded Content television modules. Specifically: “Television – By Brand” merges “National TV (Branded Content)” and “Regional Sports TV (Branded Content)” into a single module, where programming across network types can be viewed in a single chart (and can be separated using the Network Type filter if desired)“Television – By Team” replaces “TV – Team Sponsorship”, maintaining the ability to additionally filter brand exposures by the associated sports team(s). The programming in this module includes all available NFL, NBA, MLB, and NHL live games and replays across national television and regional sports networks, as well as team-specific studio shows (e.g., Warriors Postgame)“Television – Teams as Brands” replaces “National TV – Team Exposure”, maintaining the ability to view team-level exposures in sports talk and highlightsThe sidebar design across the Television – By Brand and Television – By Team modules has been evolved to accommodate additional metrics and streamline access to individual charts and tables We are excited by initial feedback to these module updates, and look forward to continuing to provide product innovation on a regular basis. Please reach out to your representative with any questions or needs as you experience the module upgrades. We look forward to your continued feedback and thank you for your trust in Mensio.
BACK TO ALL BLOGS Web Search: Visual Comparisons To Web Content Using Deep Learning HiveJune 22, 2022March 5, 2025 Contents What is Web Search?Building a Visual Comparison Engine: Hive’s Similarity Model and Search IndexHow it Works: Web Search API and Visual Search ExamplesFinal Thoughts: Future Directions for Web Search What is Web Search? Earlier this year, Hive launched a pair of API products built on new deep learning models that analyze visual similarity between images: NFT Search and Custom Search. To help platforms authenticate visual content, these APIs search unstructured datasets for similar images to uncover relationships within a larger content ecosystem. These launches marked the start of a larger effort by Hive to make broader, more relational content understanding available to enterprise customers. Now, we’re unveiling the most ambitious of our Intelligent Search services yet: Web Search, for visual comparisons to content on the open web. Web Search deploys our best-in-class image similarity model across stored media from billions of crawled web pages, retrieves visually similar versions of a query image, and returns these matches via API. The Web Search API enables automated checks against web images in a variety of use-cases, including: Detecting misuse of image assets in copyright enforcement contextsEnforcing paywalls on premium content by identifying unauthorized reposts and shares of protected mediaVerifying originality of user-generated content like profile and marketplace photos In this announcement, we’ll take a closer look at the two pillars of Hive’s visual search engine – our image similarity model and web index – and preview how the Web Search API works. Building a Visual Comparison Engine: Hive’s Similarity Model and Search Index The backbone of Web Search is Hive’s visual similarity model, a deep vision model that conducts pair-wise visual comparisons between images. Unlike typical fingerprinting algorithms, our models assess visual similarity based on high-level feature alignment to mimic (and surpass) human perceptual comparison. To build this, we used contrastive learning on image sets including substantial augmentations and negative examples, training a robust visual similarity model without relying on supervisory labels to teach specific definitions. The resulting model considers both duplicates of an image and modified versions as similar – including overlay elements, filters and edits, and adversarial modifications. For a pair of images, the model returns a normalized score between 0 and 1 correlated with the output of its contrastive loss function (i.e., based on similarity of feature vectors). A pair-wise similarity score of 1 indicates an exact visual match between images, while lower scores reflect the extent of any visual differences. A robust image comparison model is a necessary part of a visual search engine, but not entirely sufficient. For Web Search to be broadly useful, we also needed a comprehensive reference database of images to compare against. To do this, Hive built and deployed a custom web crawler to continuously retrieve image content on public pages. Since we began crawling, we’ve grown this dataset to tens of billion images, which continues to expand as our crawler encounters new web pages and freshly posted content. To enable more detailed search results, we also index URL and domain information, alt text, and other image metadata that can be returned alongside matches in the API response. Putting it Together: Web Search API and Visual Search Examples Given a query image, the Web Search API uses the similarity model to compare against all reference images in our web index and returns all matches above a threshold similarity score. For each match, the API response specifies: A direct link (URL) to the matching imageA backlink to the domain where the matching image was foundA similarity score between the query image and the match Here are responses from a few example searches that show the versatility of the Web Search API: Web Search is well-suited to help marketplaces automatically identify potential scam listings that use images taken from the open web. For example, we queried the left image from a suspiciously cheap rental ad that looked a little too good to be true. Web Search uncovered photos from a real listing for the unit on the realtor’s website. The two photos are almost identical except for slightly lower resolution in the scam image; our similarity model predicts accordingly with a similarity score of 0.99. Let’s look at another example, this time with more visually impactful differences: Here, the query image incorporates the original but uses a significant digital overlay. Still, our similarity model identifies the source image as a match with a similarity score of 0.7. The ability to recognize edited photos enables Web Search to help social and dating platforms identify impersonation attempts (“catfishing”) that use web photos on their profile, even if those photos have been noticeably modified. Here’s a similar example where the query image is “clean” and the matching image is modified with a text overlay: In this case, the matching image reuses the original with text stylized as a magazine cover, and our model correctly identifies the edited version. With similar queries, Web Search can help platforms track down misuses of stock photos and copyrighted images, or reposts of premium (paywall) content to other websites. In their own searches, platforms can use our model’s similarity scores to target duplicates or close copies at high score thresholds and/or broaden searches to visually related images at lower scores to align with their definitions and intended use-cases. Final Thoughts: Future Directions for Web Search Hive’s Visual Search APIs offer enterprise customers new insight into how their visual content is used and where it comes from with on-demand searches on their own content (Custom Search), blockchains (NFT Search), and, now, the open web (Web Search). The capabilities of our image similarity model and other content tagging models raise the bar on what’s possible in the search space. In building these datasets, we’re also thinking about ways to unlock other actionable insights within our search indexes. As a next step, we’ll be broadening our web index to include videos, GIFs, and audio data. From there, we plan to support using our targeted content tagging models – logo detectors, OCR, scene classification, and more – to enable open web searches across content modalities, content-targeted ad placements, and other use-cases in the future. To learn more about Web Search or our other visual search APIs, you can contact us here or reach out to our sales team directly.
BACK TO ALL BLOGS Updates to Hive’s Best-in-Class Visual Moderation Model HiveJune 21, 2022March 5, 2025 Contents New Visual Moderation Classes for Greater Content UnderstandingUndressedGamblingConfederate SymbolismImprovements to Established Visual Moderation ClassesFinal Thoughts Hive’s visual classifier is a cornerstone of our content moderation suite. Our visual moderation API has consistently been the best solution on the market for moderating key types of image-based content, and some of the world’s largest content platforms continue to trust Hive’s Visual Moderation model for effective automated enforcement on NSFW images, violence, hate, and more. As content moderation needs have evolved and grown, our visual classifier has also expanded to include 75 moderation classes across 31 different model heads. This is usually an iterative process – as our partners continue to send high volumes of content for analysis, we uncover ways to refine our classification schemes and identify new, useful types of content. Recently, we’ve worked on broadening our visual model by defining new classes with input from our customers. And today, we’re shipping the general release of our latest visual moderation model, including three new classes to bolster our existing model capabilities: Undressed to target racier suggestive images that may not be explicit enough to label NSFWGambling to capture betting in casinos or on games and sporting eventsConfederate to capture imagery of the Confederate flag and graphics based on its design All Hive customers can now upgrade to our new model to access predictions in these new classes at no additional cost. In this post, we’ll take a closer look at how these classes can be used and our process behind creating them. New Visual Moderation Classes For Greater Content Understanding Deep learning classifiers are most effective when given training data that illustrates a clear definition of what does and does not belong in the class. For this release, we used our distributed data labeling workforce – with over 5 million contributors – to efficiently source instructive labels on millions of training images relevant to our class definitions. Below, we’ll take a closer look at some visual examples to illustrate our ground truth definitions for each new class. Undressed In previous versions, Hive’s visual classifier separated adult content into two umbrella classes: “NSFW,” which includes nudity and other explicit sexual classes, and “Suggestive,” which captures milder classes that might still be considered inappropriate. Our “Suggestive” class is a bit broad by design, and some customers have expressed interest in a simple way to identify the racier cases without also flagging more benign images (e.g., swimwear in beach photos). So, for this release, we trained a new class to refine this distinction: undressed. We wanted this class to capture images where a subject is clearly nude, even if their privates aren’t visible due to their pose, are temporarily covered by their hands or an object, or are occluded by digital overlays like emojis, scribbles, or shapes. To construct our training set, we added new annotations to existing training images for our NSFW and Suggestive classes and sourced additional targeted examples. Overall, this gave us a labeled set of 2.6M images to teach this ground truth to our new classifier. Here’s a mild example to help illustrate the difference between our undressed and NSFW definitions (you can find a full definition for undressed and other relevant classes in our documentation): Confidence scores for unedited version (left): undressed 1.00; general_nsfw 1.00; general_suggestive 0.00. Confidence scores for edited version (right): undressed 0.99; general_nsfw 0.35; general_suggestive 0.61 The first image showing explicit nudity is classified as both undressed and NSFW with maximum confidence. When we add a simple overlay over relevant parts of the image, however, the NSFW score drops far below threshold confidence while the undressed score remains very high. Platforms can use undressed to flag both nudity and more obviously suggestive images in a single class. For content policies where milder images are allowed but undressed-type images are not, we expect this class to significantly reduce any need for human moderator review to enforce this distinction. Gambling Gambling was another type of content that frequently came up in customer feedback. This was a new undertaking for Hive, and building our ground truth and training set for this class was an interesting exercise in definitions and evaluating context in images. Technically, gambling involves a wager staked on an uncertain outcome with the intent of winning a prize. For practical purposes, though, we decided to consider evidence of betting as the key factor. Certain behavior – like playing a slot machine or buying a lottery ticket – is always gambling since it requires a bet. But cards, dice, and competitive games don’t necessarily involve betting. We found the most accurate approach to be requiring visible money, chips or other tokens in these cases in order to flag an image as gambling. Similarly, we don’t consider photos at races or sporting events to be gambling unless receipts from a betting exchange or website are also shown. To train our new class on this ground truth definition, we sourced and labeled a custom set of over 1.1M images. The new visual classifier can now distinguish between gambling activity and similar non-gambling behavior, even if the images are visually similar: For more detailed information, you can see a full description of our gambling class here. Platforms that wish to moderate or identify gambling can access predictions from this model head by default after upgrading to this model release. Confederate Symbolism Separately, many of our customers also expressed interest in more complete monitoring of visual hate and white nationalism, especially Confederate symbolism. For this release, we sourced and labeled over 1M images to train a new class for identifying imagery of the commonly used version of the Confederate flag. In addition to identifying photos of the flag itself, this new model head will also capture the Confederate “stars and bars” shown in graphics, tattoos, clothing, and the like. We also trained the model to ignore visually similar flags and historical variants that are not easily recognizable: Along with our other hate classes, customers can now use predictions from our Confederate class to keep their online environments safe. Improvements to Established Visual Moderation Classes Beyond these new classes, we also focused on improving the model’s understanding around niche edge cases in existing model heads. For example, we leveraged active learning and additional training examples to address biases we occasionally found in our NSFW and Gun classifiers. This corrected some interesting biases where the model sometimes incorrectly identified studio microphones as guns, or mistook acne creams for other, less safe-for-work liquids. Final Thoughts This release delivers our most comprehensive and capable Visual Moderation model yet to help platforms develop proactive, cost-effective protection for their online communities. As moderation needs become more sophisticated, we’ll continue to incorporate feedback from our partners and refine our content moderation models to keep up. Stay tuned for our next release with additional classes and improvements later this year. If you have any questions about this release, please get in touch at support@thehive.ai or api@thehive.ai. You can also find a video tutorial for upgrading to the latest model configuration here. For more information on Visual Moderation more generally, feel free to reach out to sales@thehive.ai or check out our documentation.
BACK TO ALL BLOGS The Race for Automotive Sponsorship HiveMay 25, 2022March 4, 2025 At a glance: Auto manufacturers earned an estimated $1.1 billion in media value from visual logo exposures on national and regional TV over the past year, with Toyota capturing the highest share of voice across programmingTV exposures for auto brands are correlated with sports seasons, with official league sponsors dominating in playoff and championship months; additionally, in the fight for share of voice on TV, auto manufacturers strategically invest in specific categories, with a different brand leader across each sportThe return on investment of different placement types can vary; for example, looking at Major League Baseball in the month of April as a case study, exposures of Toyota and Ford outweighed those of league champion ChevroletMeasuring amplification of exposures in shoulder programming and highlights – typically overlooked by sponsors today – doubles the number of unique programs with auto logos exposed and increases media value by 17% As TV viewership continues to fragment across different platforms, the ability of sponsorships to ensure brand exposure within desired content has never been more important. However, sponsorship activations themselves are fragmented across sports and rights holders (e.g., teams, leagues, broadcast partners), resulting in demand for better data to measure the effectiveness of automakers’ own investments and to monitor a dynamic competitive environment. The sponsorship landscape among automakers was analyzed using data from Mensio, Hive’s AI-powered media intelligence platform. Here’s what we learned. 1. In aggregate, auto manufacturers (OEMs) garnered an estimated $1.1 billion in media value from visual logo exposure on national and regional TV over the past year. Over 80% of this value was owned by the top 10 most exposed brands (out of a total compared dataset of 67 brands), speaking to market concentration on TV. Toyota led the pack by a large margin as the “Let’s go places” manufacturer indeed went everywhere on TV. With half the estimated media value of Toyota, Ford was the second-highest earning OEM, followed by Kia, Honda, and Chevrolet. 2. While sports sponsorships are typically rooted in the objective of aligning automakers’ brands with a given sport and/or team and its fans, sports sponsorship also dictate the time of year when different brands capture outsized share of voice. Each of the official league sponsors of the four largest US sports leagues experienced spikes in their share of voice of in-content brand exposures during playoff and championship periods: Kia (NBA) in April/May, Honda (NHL) in June/July, Chevrolet (MLB) in October, and Toyota (NFL) in February (its most exposed month, despite high visibility throughout the year). Additionally, Mercedes-Benz’ sponsorship of the U.S. Open rockets up its exposures in the month of September, with over $2M in estimated media value from the Men’s Championship match alone. 3. In the fight for share of voice within in-content brand exposure on television, brands placed differing bets across genres. Within sports, a different brand dominates each league, with official league partners leading the way. However, exposure wasn’t limited to league partners; Ford’s sponsorship of NFL pre-game programming is one example among many team- and broadcast-level activations where brands have competed for share of voice within a sport outside of official league sponsorships. Outside of sports, other genres of entertainment saw other investments by auto companies, such as Mercedes-Benz’ top feature in talk shows and awards/special programming. 4. Zooming in on the first month of the 2022 Major League Baseball season across national TV and regional sports networks presents an interesting early season case study. While Chevrolet – the official league sponsor – will likely increase exposures as the season continues, the brand started the year ranked #3 in share of voice for in-game brand exposures. Heavy team and broadcast sponsorship investments made by Toyota and Ford outweighed Chevy, illustrating alternate tactics to reach the same audience at different points during the season. 5. Given the massive investment and competition for the best placements, it is important for brands to fully measure their onscreen exposures. Currently, most brands are limited to “whistle to whistle” measurement focusing on in-game exposures, and sometimes the additional exposure from social media. The fragmentation of shoulder programming and highlights has traditionally been difficult to measure at scale; however, doing so provides a far more comprehensive understanding of performance from a given activation. Using always-on measurement from Mensio, which reports across every second of every program from 100+ national TV networks and regional sports networks, we estimate that amplification from shoulder programming and highlights almost doubles the number of unique programs with auto brand logos exposed, increasing duration of in-content brand exposures by 32% and the associated equivalent media value by 17%. Credible competitive intelligence data is critical in making decisions on the best sponsorship placements. Mensio, Hive’s AI-powered media intelligence platform, provides always-on measurement of in-content brand exposure for more than 7,000 brands across 24/7 programming from 100+ national TV channels and regional sports networks. Access to credible competitive intelligence data is critical for branded content and sponsorship decisions. Mensio allows brands to understand how their share of voice compares to that of competitors at the program-level and in aggregate. For more information on Mensio or to schedule a demo and learn how Mensio can support your brand, reach out to Hive at demo@thehive.ai. Note: This analysis looked at in-program auto manufacturer logo exposures from May 2021 to April 2022 on national and regional TV (excluding commercials) and includes Tier 1, Tier 2, and Tier 3 placements.