AI is a bit of a black box when it comes to brand protection and risk protection. Let’s open the black box together. With Gen AI in our phones, our inboxes, and our workflows, it’s increasingly in every marketing pitch too. While the technology promises disruption and innovation, too often it’s deployed as a buzzword instead of a benefit. For brands, it’s hard to know what actually delivers, and what’s just smoke and mirrors.
AI should deliver value. Beyond creating shiny marketing material, let’s explore some real use cases with practical and measurable benefits for your brand. Let’s also explore Deep Vision, EBRAND’s new AI-powered detection engine that brings clarity to online brand protection. This isn’t just another image recognition tool. As we’ll discuss, Deep Vision combines optical character recognition (OCR), contextual analysis, and feature matching to find what others miss. Whether it’s a counterfeit listing with your logo removed or a second-hand sale pretending to be new, Deep Vision sees through it—and lets you act.
1. Optical Character Recognition (OCR): Reading what others miss
Online listings don’t always include obvious brand names or trademarks. Counterfeiters strip logos from images, or crop photos just enough to avoid detection. An effective OCR engine scans inside image and reads embedded text, using AI contextual analysis to detect evasive infringements. With Deep Vision’s recognition engine, your searches deliver results, even when the logo or the text isn’t indexed or immediately visible. This means you can find infringements that traditional keyword systems completely overlook.
With Deep Vision, if someone removes your logo from a box but leaves clues in the background, we’ll spot it. Our OCR doesn’t just read—it interprets context. That gives your enforcement team a decisive edge.
2. Context-aware AI: Find your product without the keywords
A plush toy listed as “soft animal” might never show up in your old keyword-based systems. But Deep Vision understands context. Its AI models detect products based on sentiment, texture, and object-level features—not just metadata.
Search for “purple plush toy,” and Deep Vision will show you images that match that concept, even if the title says nothing of the sort. It bridges the gap between what a product looks like and how it’s described, making detection more human—and more effective.
3. Image uploads: Match products, no matter the angle
Brand owners can now upload images of their own products into Deep Vision. The system then scans online listings to detect visual matches, even if the orientation, lighting, or background has changed.
This is especially powerful when counterfeiters slightly adjust images to avoid detection. Unlike basic reverse image tools, Deep Vision looks deeper, recognizing a product even when it’s been flipped, rotated, or edited.
4. Segmentation: Focus on what matters
Sometimes you don’t need to match a whole image—you just need to check the logo placement, or packaging style. Deep Vision’s segmentation tool lets you highlight a region within an image as your reference point. That region then becomes the focus of the search.
This feature feeds into a broader risk scoring mechanism, helping your team prioritize high-risk matches and act quickly. It’s precise. It’s fast. And it’s powered by AI tuned for enforcement.
5. Multilingual search: AI without translation gaps
Traditionally, you needed to translate keywords into dozens of languages to cover global marketplaces like eBay or AliBaba. Deep Vision changes that. It supports image and context-based detection across languages, letting you search in one language and still surface relevant listings everywhere.
For brand protection teams spread across regions, this means massive time savings—and stronger global coverage.
6. Categorisation by product type and condition
Counterfeits aren’t just new. They’re often disguised as “used,” or resold as “refurbished.” Deep Vision uses AI to detect visual signals that indicate whether a product is new, used, or repackaged—based solely on image data.
That insight is crucial when enforcing resale policies or tackling grey market activity. You can now distinguish between a legitimate used listing and a counterfeit falsely labelled as pre-owned.
7. Advanced image similarity: Beyond basic matching
Deep Vision’s image similarity engine goes well beyond reverse search. It compares uploaded images using dense vector models that assess structure, shape, and visual features on multiple levels.
This allows for detection of items that look similar—even when they aren’t exact copies. We combine smart feature matching technology to give you a fuller picture of infringing activity. For spotting subtle copycats or near-duplicates, this AI system leads the pack.
8. Integration with cybersecurity and risk protection
Deep Vision doesn’t work in isolation. It feeds data into our ARGOS platform, linking visual detections with keyword signals, domain intelligence, and identity strings. We blend this data with insights from our Digital Risk Protection platform to match brand variations and string deviations—closing the loop between visual, textual, and behavioural signals.
The result is a smarter, more comprehensive brand protection system, that’ll deliver the insights you need to detect and eliminate online infringements.
Conclusions: Turning AI into valuable brand protection
Too many providers talk about AI like it’s magic. We don’t. We show you what it can do in real terms—and then we use it to solve real problems. Deep Vision is designed to pull AI out of the black box and put it in your hands.
Want to see what it actually does? We’ll show you.
You’ve now seen the tactics, and the tools. Deep Vision is live, and already uncovering threats other systems miss. It’s time to apply this technology to your brand. We’ll start with a free audit —powered by Deep Vision—and explore exactly how counterfeiters are targeting your products online.
Let’s expose them, together.
