Despite often being considered a “fledgling technology,” computer vision is already proving itself valuable in multiple scenarios. The success of computer vision in a range of fields has seen many businesses adopt the technology to fulfill a multitude of purposes in their tech stack.
A common pathway into computer vision is as a SaaS solution. However, as businesses unlock the full potential of computer vision and become more reliant on the technology, the practicality of this model lessens. The need to process potentially millions of images daily can strain internal and external network infrastructure.
In such circumstances, on-premise in computer vision can be a more viable option. This article discusses the importance of on-premise computer vision and looks at the applications and use-cases that can benefit from it.
Before discussing use cases of on-premise computer vision, it is worth looking at a brief overview of computer vision in general and why on-premise computer vision is an important development.
Put simply, computer vision is the use of AI to assess images and draw relevant data from them. What is considered relevant data is determined by the scope of the job. Common uses include anti-phishing security measures, copyright protection, counterfeit detection, and visual content moderation.
The ability and necessity for the process to be run on millions of images is why many firms are now turning to on-premise for their computer vision solutions.
Latency delays, network bottlenecks, and cost efficiencies are a few of the areas where major benefits are made when companies make the switch.
In the following section, we look at some specific use cases and discuss how employing on-premise computer vision will be of benefit.
One area where computer Vision is making huge inroads is as a valuable security layer in the arms race against “Bad Actors.”
Phishing attacks are costing businesses and individuals billions of dollars annually. This is likely to worsen as increasingly sophisticated methods used in phishing attacks are making successful detections more difficult. It is also the case that “bad actors” only have to get lucky once.
Computer Vision does not rely on blacklists or the detection of embedded threats. Rather it uses AI to detect threats using visual clues, an excellent use case describing how Visual Phishing Detection works can be found here.
For many businesses, running this as a SaaS solution works. But there can be limitations, and this is where the benefits of on-premise computer vision come into play. The importance of on-premise computer vision in anti-phishing is covered in detail elsewhere on the site, but below is a brief summary of the benefits.
With potentially millions of images being scanned daily, latency delays caused by slow network connections are eliminated.
By deploying an organization’s own infrastructure to process the images, resources can be allocated as required as requirements fluctuate. This isn’t always a practical option with SaaS solutions that function most economically when consistent amounts of data are processed.
Systems such as Visua’s On-Premise computer vision have been developed to be “server and hardware agnostic”. This negates the need for costly hardware upgrades and implementation.
Let’s start with a startling statistic. According to the Organization for Economic Co-operation and Development (OECD), it is estimated that 5% of world trade involves counterfeited goods.
Until recently, the detection of counterfeit goods was always a game of catch-up. Such is the scale of the problem, that it has – until now – been in the realm of the impossible to identify even a tiny fraction of this flood of counterfeited material.
This is another area where the deployment of on-premise computer vision has been a real game-changer. The importance of computer vision in the fight against counterfeiting can help swing the balance back in favor of businesses. Amongst the benefits it brings to the table are:
On-premise computer vision gives businesses the ability to scan massive amounts of data as demand dictates.
There are reasonable concerns about exposing highly sensitive data to extra risks. By implementing an on-premise computer vision anti-counterfeiting solution, sensitive data is always kept on the local network.
SaaS solutions are perfect for many organizations, but for those with flexible demand and irregular processing requirements, on-premise computer vision offers a more flexible approach that is easily tailored to match demand.
In a world awash with visual content, the problem of content moderation is something that has grown beyond human capabilities. Illicit or inappropriate content in any form appearing on any channel can do untold harm to a business’s reputation.
On-premise computer vision can give any organization a human eye’s perspective at computer speed. Gaming platforms, video content, social media channels, and even the emerging world of the metaverse can all be effectively “content-policed” using computer vision.
A recent article in The Conversation shows both the scale of the problem and the importance of taking a flexible and reactive approach to content monitoring. The article details what can happen when “fake content” isn’t detected.
It also highlights why the flexible approach that on-premise offers is important. This is down to the sheer volume of visual content that is uploaded. In total, each day, over 720,000 hours of video and 3.2 billion images are uploaded.
SaaS computer vision solutions are tailored to work best for organizations that process large amounts of data regularly. More specifically, SaaS works best when batches of data are processed as a whole, with the results also returned as a batch.
However, with the sheer volume of images and videos getting uploaded, content monitoring is often required to be run on a real-time basis. In these instances, an on-premise computer vision solution is often a more cost-effective option.
Network latency can also impact the effectiveness of computer vision as a content moderation tool. Once again, the need to react quickly to inappropriate content is critical here. The wrong content can go viral within seconds of being posted, and organizations need to react in the same timescale. The reduced latency of on-premise computer vision allows this.
As already mentioned, the scale of counterfeiting in global trade is immense. Against this backdrop, it is understandable that copyright and trademark owners are highly protective of any copyright infringement.
For platform operators, this leaves them exposed to potential lawsuits because of unintentional and third-party trademark infringements. It also causes headaches for the copyright protection service sector, which is faced with a mountain of data beyond the scope of any form of human scanning.
On-premise computer vision, however, does have the ability to scan at the scale required. For some organizations, the SaaS version of computer vision is adequate, however, there are advantages for those wanting to implement an onsite solution, including:
The sheer quantity of data, when paired with the requirement for frequent scanning, can make the SaaS solution impractical. Fraudsters, scammers, and counterfeiters often work within short windows of opportunity. Batch processing delays may be the only time they need to make the most of the misuse of protected copyrights or trademarks.
The same requirement for real-time results could make on-premise computer vision a more economical option for many organizations. As already noted, SaaS works best where large batches of data are processed in a block with the results returned in a batch.
This scenario often doesn’t fit with organizations that need to be reactive and produce real-time results, which is the case for many platform operators and protection services.
In these cases, SaaS costs can be difficult to quantify and quickly begin to stretch budgets.
Economies can also be made when implementing on-premise computer vision. The Visual-AI platform has been developed to integrate seamlessly within existing infrastructure regardless of server and existing hardware.
The above list cannot be called comprehensive by any means. Computer vision is an emerging technology, and we are only just scratching the surface of its true potential. Included in other sectors where it is being deployed successfully are:
On-premise computer vision is a technology that can benefit a huge range of enterprises and organizations. It provides real-time intelligence, data, and extra layers of security in a platform that will integrate seamlessly into any hardware infrastructure.
There is a lot more information on the subject of computer vision and how it can help any tech stack on our blog posts and video posts.
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