You know that we are living in a visual world and we are the Visual-AI People! From the world around us to the media we consume, including online, most of the information we receive is visual. If humans were computers, 80% of the data our systems processed would be from visual sources. Seed scientific tells us we watch 5 billion YouTube videos every day, almost 700,000 hours of Netflix a year and that 14% of the world’s population has an Instagram account.
With this in mind, it’s no surprise that more and more businesses are trying to understand how Visual-AI can enhance their users’ experiences, improve operational efficiency and optimize data reporting.
Visual Artificial Intelligence is an aspect of computer science that teaches machines to make sense of images and visual data the same way people do. It is also often referred to as computer vision. Visual-AI enables machines not just to see, but to also understand and derive meaning behind images and video in accordance with the applied algorithm.
One example of this; they can categorize objects in a single image, labeling each object correctly as a desk, a plant, a pizza and so on, by comparing them to images in its library, or memory, just as a person would. It might sound futuristic and perhaps even outlandish but Visual-AI is the technology that enables a number of things that have become part of our everyday lives. QR code scanning, visual search on shopping apps and facial recognition screen unlock are all powered by Visual-AI.
The continued advancement of Visual Artificial Intelligence has led to the development of new technologies in a huge range of fields from marketing to sports, healthcare to security, automotive to retail and eCommerce. We’re seeing incredible innovation empowered by Visual-AI that not only enhances user experience and operational efficiency but which will also make a difference in the grander scheme of things.
One such use case is the introduction of Visual-AI to Phishing Protection software. As cybercriminals increasingly use visuals to evade detection and with the continuing rise of brand spoofing, cybersecurity software developers are looking to computer vision to increase protection for their users.
Phishing Detection Visual-AI is developed in such a way that is easily integrated to work with a platform’s existing detection methods. It helps to provide an early warning system that detects high-risk brands and other visual signals such as forms, trust icons and words that have been rendered as images. Traditional programmatic analysis simply cannot detect these kinds of threats.
Because of Visual-AI, it’s now possible to stop more phishing attacks than ever before.
Visual-AI has the potential to make the online world infinitely safer for users, thus protecting the integrity of online platforms like social media websites, video sharing sites, messaging apps and so on.
While moderating text is an important aspect of protecting users, image and video moderation is absolutely essential to make these platforms safe environments, free from abusive and especially horrifying content.
Image moderation utilizes object detection by analyzing the media for items that may be unsuitable or harmful, such as weapons, drug paraphernalia and excessive/gratuitous nudity, etc. Text detection takes things a step further by detecting potentially offensive or harmful words included in the frame that natural language processing alone would miss. Video moderation uses the same technologies, analyzing the video frame by frame for offending visuals. With live streaming becoming increasingly common across all social apps in particular, the ability to process in real-time, without introducing lag has become essential in content moderation.
Content Moderation can also be deployed by marketplaces to prevent the sale of inappropriate, illegal and highly offensive materials that may be listed covertly. As we have outlined in these pages before, marketplaces are ultimately legally and reputationally responsible for the content sold on their platform by third parties.
In order to prevent potential legal action or reputation damage, Visual-AI can be deployed to monitor the designs and products uploaded to the platform. Using an array of computer vision technology, the API can be trained to detect specific logos to prevent copyright cases, terms deemed as racist, misogynist, homophobic, etc., and items that are legally controlled.
Other examples of Visual-AI in action
These are just two examples of how Visual Artificial intelligence is making a true difference. Other examples of Visual-AI in action include:
Learn how computer vision works with a variety of platform types in our blog post which discusses computer vision use cases extensively.
One of the most exciting and enhancing aspects of computer vision, or Visual-AI, is the delivery of real-time intelligence. Having access to real-time data means that action can be taken immediately, or even automatically in some cases. This can be especially powerful in cases of content moderation and phishing detection as more often than not, quick action is imperative in these and countless other cases. It’s also invaluable in other use cases that demand immediate reporting such as social listening, sponsorship monitoring, and more.
When choosing a visual artificial intelligence provider for your platform or project, it’s important to understand exactly what you need from the AI. There are a number of computer vision applications and APIs available, and it can be hard to know which ones will provide exactly what you.
We have gathered some information on the market’s four leading Visual-AI providers, Amazon Rekognition, Google Cloud Vision, Microsoft’s Azure Computer Vision, and our own offering here at VISUA. Taking a look at each organization’s overall offering and analyzing key points of interest, we hope to save some time on research so you can kick start your project perhaps a little sooner than expected.
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