What we did

Using computer vision to automate manual processes

DiUS explored various options to build an end-to-end machine learning solution that would automatically detect unauthorised copyrighted logos, as well as inappropriate and offensive language or images. Detecting logos by itself is a classic object detection problem within the computer vision domain, but sometimes there is more to the problem than just simply detecting a logo. The fact that some copyrighted logos are referenced in some artworks does not necessarily mean that a particular artwork is infringing. 

Some artists reference and tease brands while transforming them visually to an extent that makes the artwork non-infringing. It is then up to the human reviewer to make the call as to whether a reference of a copyrighted logo is “transformative” enough or not. This makes moderating the content in such a scenario really tricky and subjective. 

A Proof of Concept was established to compare two off-the-shelf products. For this particular use case, Visua was chosen to detect imagery that could be considered offensive. The team has since been working on a text detection solution so that both imagery and text can be automatically reviewed together. DiUS was a big part of designing and implementing the image recognition solution and rolling it out to production. Here is a summary of what DiUS did: 

Technical assessment and high-level design 

The implementation of the third-party solution was carried out by first assessing the existing tech stack and learning about the technical limitations. 

Detailed solution design 

We had to take into account a few major considerations for coming up with the final version of the detailed design, the most important of which was the ability to cope with the load of constantly incoming artworks and their associated images that needed to be processed. Moreover, we had to make sure our design was scalable in the sense that it would allow adding other vetting mechanisms like text processing later on. 

Managing interactions with the third-party service provider 

An important part of designing and implementing the image recognition solution was to provide the baseline data to VISUA to train its machine learning models for future predictions. DiUS developed scripts and managed collaborations with the VISUA team to make sure the training data is prepared in the cleanest and most usable way possible and also drove brainstorming sessions with VISUA to tackle challenges along the way. 

Implementation and rollout 

Software and ML consultants at DiUS were in charge of implementing the back-end image recognition solution as well as the front-end components of the moderation portal in collaboration with other engineering teams from Redbubble. 

Following the success of this content moderation system, the team is now implementing a similar approach on the use of trademarks and the adherence of trademark guidelines.