Category: Blog Post

Contents

• New Visual Moderation Classes for Greater Content Understanding
  • Undressed
  • Gambling
  • Confederate Symbolism
• Improvements to Established Visual Moderation Classes
• Final 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 NSFW
• Gambling to capture betting in casinos or on games and sporting events
• Confederate 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): 

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. 

Contents

• Content Moderation Challenges in the Aftermath of Buffalo
• Conventional Moderation and Copy Detection Approaches
• Deep Learning to Advance Video Moderation and Contain Viral Content
• How it works: Deep Learning Models in Automated Content Moderation Systems
• Final Thoughts: How Hive Can Help

Content Moderation Challenges in the Aftermath of Buffalo

The racially-motivated shooting in a Buffalo supermarket – live streamed by the perpetrator and shared across social media – is tragic on many levels.  Above all else, lives were lost and families are forever broken as a result of this horrific attack.  Making matters worse, copies of the violent recording are spreading on major social platforms, amplifying extremist messages and providing a blueprint for future attacks.

Unfortunately, this is not a new problem: extremist videos and other graphic content have been widely shared for shock value in the past, with little regard for the negative impacts. And bad actors are more sophisticated than ever, uploading altered or manipulated versions to thwart moderation systems.

As the world grapples with broader questions of racism and violence, we’ve been working with our partners behind the scenes to help control the spread of this and other harmful video content in their online communities.  This post covers the concerns these partners have raised with legacy moderation approaches, and how newer technology can be more effective in keeping communities safe. 

Conventional Moderation and Copy Detection Approaches

Historically, platforms relied on a combination of user reporting and human moderation to identify and react to harmful content. Once the flagged content reaches a human moderator, enforcement is usually quick and highly accurate. 

But this approach does not scale for platforms with millions (or billions) of users.  It can take hours to identify and act on an issue, especially in the aftermath of a major news event when post activity is highest.  And it isn’t always the case that users will catch bad content quickly: when the Christchurch massacre was live streamed in 2019, it was not reported until 12 minutes after the stream ended, allowing the full video to spread widely across the web.

More recently, platforms have found success using cryptographic hashes of the original video to automatically compare against newly posted videos.  These filters can quickly and proactively screen high volumes of content, but are generally limited to detecting copies of the same video. Hashing checks often miss content if there are changes to file formats, resolutions, and codecs. And even the most advanced “perceptual” hashing comparisons – which preprocess image data in order to consider more abstract features – can be defeated by adversarial augmentations.  

Deep Learning To Advance Video Moderation and Contain Viral Content

Deep learning models can close the moderation capability gap for platforms in multiple ways. 

First, visual classifier models can proactively monitor live or prerecorded video for indicators of violence.  These model predictions enable platforms to shut down or remove content in real-time, preventing the publishing and distribution of videos that break policies in the first place.  The visual classifiers can look for combinations of factors, such as someone holding a gun, bodily injury, blood, and other object or scene information to create automated and nuanced enforcement mechanisms. Specialized training techniques can also accurately teach visual classifiers to identify the difference ​​between real violence and photorealistic violence depicted in video games, so that something like a first-person shooter game walkthrough is not mistaken for an real violent event.

In addition to screening using visual classifiers, platforms can harness new types of similarity models to stop reposts of videos confirmed to be harmful, even if those videos are adversarially altered or manipulated. If modified versions somehow bypass visual classification filters, these models can catch these videos based on visual similarity to the original version.   

In these cases, self-supervised training techniques expose the models to a range of image augmentation and manipulation methods, enabling them to accurately assess human perceptual similarity between image-based content. These visual similarity models can detect duplicates and close copies of the original image or video, including more heavily modified versions that would otherwise go undetected by hashing comparisons.

Unlike visual classifiers, these models do not look for specific visual subject matter in their analysis.  Instead, they quantify visual similarity on a spectrum based on overlap between abstract structural features. This means there’s no need to produce training data to optimize the model for every possible scenario or type of harmful content; detecting copies and modified versions of known content simply requires that the model accurately assess whether images or video come from the same source.

How it works: Deep Learning Models in Automated Content Moderation Systems

Using predictions from these deep learning models as a real-time signal offers a powerful way to proactively screen video content at scale. These model results can inform automated enforcement decisions or triage potentially harmful videos for human review. 

Advanced visual classification models can accurately distinguish between real and photorealistic animated weapons. Here are results from video frames containing both animated and real guns. 

To flag real graphic violence, automated moderation logic could combine confidence scores in actively held weapons, blood, and/or corpse classes but exclude more benign images like these examples. 

As a second line of defense, platforms need to be able to detect reposts or modified versions of known harmful videos from spreading.  To do this, platforms use predictions from pre-trained visual similarity models in the same way they use hash comparisons today. With an original version stored as a reference, automated moderation systems can perform a frame-wise comparison with any newly posted videos, flagging or removing new content that scores above a certain similarity threshold.  

In these examples, visual similarity models accurately predict that frame(s) in the query video are derived from the original reference, even under heavy augmentation. By screening new uploads against video content known to be graphic, violent, or otherwise harmful, these moderation systems can replace incomplete tools like hashing and audio comparison to more comprehensively solve the harmful content detection problem.

Final Thoughts: How Hive Can Help

No amount of technology can undo the harm caused by violent extremism in Buffalo or elsewhere.  We can, however, use new technology to mitigate the immediate and future harms of allowing hate-based violence to be spread in our online communities. 

Hive is proud to support the world’s largest and most diverse platforms in fulfilling their obligation to keep online communities safe, vibrant, and hopeful. We will continue to contribute towards state-of-the-art moderation solutions, and can answer questions or offer guidance to Trust & Safety teams who share our mission at support@thehive.ai.