Yesterday, we announced that Two Hat has acquired image moderation service ImageVision. With the addition of ImageVision’s technology to our existing image recognition tech stack, we’ve boosted our filter accuracy — and are determined to push image moderation to the next level.

Today, Two Hat CEO and founder Chris Priebe discusses why ImageVision was the ideal choice for a technology acquisition— and how he hopes to change the landscape of image moderation in 2019.

We were approached by ImageVision over a year ago. Their founder Steven White has a powerful story that led him to found the company (it’s his to tell so I won’t share). His story resonated with me and my own journey of why I founded Two Hat. He spent over 10 years perfecting his art. He had clients with Facebook, Yahoo, Flickr, and Apple. That is 10 years of experience and over $10 million in investment to solve the problems of accurately detecting pornographic images.

Of course 10 years ago we all did things differently. Neural networks weren’t popular yet. Back then, you would look at how much skin tone was in an image. You looked at angles and curves and how they relate to each other. ImageVision made 185 of these hand-coded features.

Later they moved on to neural networks but ImageVision did something amazing. They took their manually coded features and fed both them and the pixels into the neural network. And they got a result different from what everyone else was doing at the time.

Now here is the reality — there is no way I’m going to hire people to write nearly 200 manually coded features in this modern age. And yet the problem of child sexual abuse imagery is so important that we need to throw every resource we can at it. It’s not good enough to only prevent 90% of exploitation — we need all the resources we can get.

Like describing an elephant

So we did a study. We asked, “What would happen if we took several image detectors and mixed them together? Would they give a better answer than any alone?”

It’s like the story of several blind men describing an elephant. One describes a tail, another a trunk, another a leg. They each think they know what an elephant looks like, but until they start listening to each other they’ll never actually “see” the real elephant. Likewise in AI, some systems are good at finding one kind of problem and another at another problem. What if we trained another model (called an ensemble) to figure out when each of them is right?

For our study, we took 30,000 pornographic images and 55,000 clean images. We used ImageVision images since they are full of really hard ones to find; the kind of images you might actually see in real life and not just a lab experiment. The big cloud providers found between 89-98% of pornographic images out of all 30k images, while the precision rate was around 95-98% for all of them (precision refers to the proportion of positive identifications that are correct).

We were excited that our current system found most of the images, but we wanted to do better.

For the CEASE.ai project, we had to create a bunch of weak learners to find CSAM. Detecting CSAM is such a huge problem that we needed to throw everything we could at it. So we ensembled the weak learners all together to see what would happen — and we got another 1% of accuracy, which is huge because the gap from 97% to 100% is the hardest to close.

But how do you close the last 2%? This is where millions of dollars and decades of experience are critical. This is where we must acquire and merge every trick in the book. When we took ImageVision’s work and merged it with our own, we squeezed out another 1%. And that’s why we bought them.

We’re working on a white paper where we’ll present our findings in further detail. Stay tuned for that soon.

The final result

So if we bought ImageVision, not only would we gain 10 years of experience, multiple patents, and over $10 million in technology, but we would be the best NSFW detector in the industry. And if we added that into our CSAM detector (along with age detection, face detection, body part detection, and abuse detection) then we could push that accuracy even closer and hopefully save more kids from the horrors of abuse. Spending money to solve this problem was a no-brainer for us.

Today, we’re on the path to making AI invisible.

Learn more about Priebe’s groundbreaking vision of artificial intelligence in an on-demand webinar. He shares more details about the acquisition, CEASE.ai, and the content moderation trends that will dominate 2019. Register to watch the webinar here.

Further reading:

Part One of The Future of Image Moderation: Why We’re Creating Invisible AI
Official ImageVision acquisition announcement.

In December and early January, we teased exciting Two Hat news coming your way in the new year. Today, we’re pleased to share our first announcement of 2019 — we have officially acquired ImageVision, an image recognition and visual search company. With the addition of ImageVision’s groundbreaking technology, we are now poised to provide the most accurate NSFW image moderation service in the industry.

We asked Two Hat CEO and founder Chris Priebe to discuss the ambitious technology goals that led to the acquisition. Here is part one of that discussion:

The future of AI is all about quality. Right now the study of images is still young. Anyone can download TensorFlow or PyTorch, feed it a few thousand images and get a model that gets things right 80-90% of the time. People are excited about that because it seems magical – “They fed a bunch of images into a box and it gave an answer that surprisingly right most of the time!” But even if you get 90% right, you are still getting 10% wrong.

Think of it this way: If you do 10 million images a day that is a million mistakes. A million times someone tried to upload a picture that was innocent and meaningful to them and they had to wait for a human to review it. That is one million images humans need to review. We call those false positives.

Worse than false positives are false negatives, where someone uploads an NSFW (not safe for work) picture or video and it isn’t detected. Hopefully, it was a mature adult who saw it. Even if it was an adult, they weren’t expecting to see adult content, so their trust in the site is in jeopardy. They’re probably less likely to encourage a friend to join them on the site or app.

Worse if it was a child who saw it. Worst of all if it is a graphic depiction of a child being abused.

Protecting children is the goal

That last point is closest to our heart. A few years ago we realized that what really keeps our clients awake at night is the possibility someone will upload child sexual abuse material (CSAM; also known as child exploitive imagery, or CEI, and formerly called child pornography) to their platform. We began a long journey to solve that problem. It began with a hackathon where we gathered some of the largest social networks in the world with international law enforcement and academia all in the same room and attempted to build a solution together.

So AI must mature. We need to get beyond a magical box that’s “good enough” and push it until AI becomes invisible. What do I mean by invisible? For us, that means you don’t even notice that there is a filter because it gets it right every time.

Today, everyone is basically doing the same thing, like what I described earlier — label some NSFW images and throw them at the black box. Some of us are opening up the black box and changing the network design to hotrod the engine, but for the most part it’s a world of “good enough”.

Invisible AI

But in the future, “good enough” will no longer be tolerated. The bar of expectation will rise and people will expect it to just work. From that, we expect companies to hyper-specialize. Models will be trained that do one thing really, really well. Instead of a single model that answers all questions, instead, there will be groups of hyper-specialists with a final arbiter over them deciding how to best blend all their opinions together to make AI invisible.

We want to be at the top of the list for those models. We want to be the best at detecting child abuse, bullying, sextortion, grooming, and racism. We are already top of the market in several of those fields and trusted by many of the largest games and social sharing platforms. But we can do more.

Solving the biggest problems on the internet

That’s why we’ve turned our attention to acquiring. These problems are too big, too important to have a “not built here, not interested” attitude. If someone else has created a model that brings new experience to our answers, then we owe it our future to embrace every advantage we can get.

Success for me means that one day my children will take for granted all the hard work we’re doing today. That our technology will be invisible.

In part two, Chris discusses why ImageVision was the ideal choice for a technology acquisition— and how he hopes to change the landscape of image moderation in 2019.

Sneak peek:

“It’s like the story of several blind men describing an elephant. One describes a tail, another a trunk, another a leg. They each think they know what an elephant looks like, but until they start listening to each other they’ll never actually “see” the real elephant. Likewise in AI, some systems are good at finding one kind of problem and another at another problem. Could we train another model (called an ensemble) to figure out when each of them is right?”

Read the official ImageVision acquisition announcement