It has become increasingly easier because of machine learning frameworks and pre-trained models, however it still demands some level of technical knowledge & infrastructure. Platforms like TensorFlow by Google and PyTorch by Facebook come with some solid machine learning libraries, making it quite easy to integrate NSFW AI for content moderation. NSFW AI is accessible to an even larger niche of mid-sized companies and startups, as a typical setup time for teams with experience in machine learning per Stanford University’s own experiments on their basic models ranges between 2–4 weeks.
But easiness of implementation varies according to the accuracy needed and extent to which AI needs context. Pre-trained NSFW AI models off-the-shelf (like in Amazon Rekognition or nsfw ai): These are pre-provisioned on large datasets and so deployments can be faster. They have DB boxes above 90% detection and are for companies who want a quick solution. For platforms hosting a significant number of images and videos, Amazon’s AI team reports that their Rekognition model can be quickly integrated through the same API that developers are already familiar with–IDEALLY allowing fast implementation for near-immediate content moderation.
The downside is that this makes implementation a bit more complex due to its being customizable. Companies that need highly specialized filtering criteria, or are context-aware in unusual ways—such as military contractors using drones to bomb Middle East enemy of the months—are going to have more than a few questions over whether additional training is actually required. Because this customization can easily take weeks to implement into an integration and there must be labeled data that the AI is exposed in order for it to “learn” how niche content should actually be handled. According to the International Association for AI Moderation, these custom-trained models generally result in a 20% bump on accuracy when dealing with specialized content but can nearly double time of implementation due to the difficulty training and validating.
An equally important aspect with respect to the ease of implementation is computing infrastructure. Media data in huge quantities requires enormous computing power, whilst the real-time implies even worse consequences for processing. Only organizations that have a lot of resources and money can afford to set up their NSFW AI systems, but cloud types like those by Google Cloud or AWS offer natively scalable setups so its solutions are very important. Cloud platforms allow you to perform real-time content moderation without purchasing expensive hardware. One MIT study on the scalability of AI shed some light into this realm: cloud-based AI implementations can reduce total setup costs by as much as 40% compared with an on-premise model, creating opportunities for businesses large and small to take advantage of NSFW filtering.
Also the requirements of user privacy and data compliance such as GDPR in Europe make it more difficult to implement NSFW AI. It is essential to make sure that AI system processes images and videos without keeping or misusing any sensitive data. This is still a large change to 3rd party dependencies need additional setup for privacy controls e.g. encryption and data anonymization Finally, even though these features make data security more robust, they can increase the implementation cost by 10-15% (Data Protection Magazine).
Even though simple NSFW AI models are easy to build out-of-the-box with some tweaking, getting good accuracy and compliance needs a bit more customisation and infrastructure. By continuing to trend towards better machine learning tools and cloud solutions on the market, deploying your own nsfw ai might even become more achievable as a scalable solution regardless of size.