Boosting Image Theft Detection Accuracy From 65% to 88%
A custom computer vision search engine that raised image-similarity precision and cut manual review by 66%.
At a Glance
The client's image-matching platform produced frequent false positives and false negatives, forcing manual review in nearly every case. Maruti Techlabs rebuilt the detection engine to measure true visual similarity instead of a simple yes-or-no classification.
Industry
Legal
Function
AI, Machine Learning & Computer Vision
Solution
Custom Computer Vision Image Search Engine
Engagement
Feasibility Study Through Model Development & Deployment
The Client
Our client is an online platform for photographers and artists to find where and how their images are used online, and it has helped more than 100,000 artists and agencies uncover and resolve copyright infringement claims worldwide through tech and legal support.
The Challenges
Guesswork Where Certainty Was Needed
The client needed a system that measured true image similarity instead of guessing, so artists could trust results without manually reviewing every case.
Low-Precision Model Producing Frequent False Results
The platform's logistic regression model delivered under 65% precision, generating frequent false positives and false negatives. Every incorrect match required manual review, slowing case resolution and undermining user trust in the platform's accuracy.
Binary Classification Couldn't Measure True Image Similarity
The existing model only classified images as matches or non-matches. The client needed a system that measured the exact degree of visual similarity between images, rather than returning a simple positive or negative result.
No Fast Way to Search and Index Images
Comparing uploaded images against millions of others online required faster searching and indexing. The platform lacked hashing or fingerprinting capabilities needed to scale image comparison efficiently across a growing photo database.
Limited File Format Support Restricted Platform Usability
The client needed to support a wider range of file formats, including GIF, PNG, JPG, JPEG, TIFF, WEBP, and PDF, to serve photographers uploading images from different tools and platforms.
Detect unauthorized use of your images with a computer vision engine trained for precision, not just classification.
Stop Guessing. Start Matching
The Solution
A Computer Vision Engine Built for Precision
The solution combined dataset preparation, a five-layer detection architecture, and cloud deployment to deliver a scalable, high-precision image search engine.
We built a five-layer architecture that preprocesses images, extracts and hashes features, indexes them for fast retrieval, and scores similarity, giving the client a scalable engine instead of a simple classifier.
We built a five-layer architecture that preprocesses images, extracts and hashes features, indexes them for fast retrieval, and scores similarity, giving the client a scalable engine instead of a simple classifier.
The Journey
Feasibility to Deployment
Business Outcomes
Sharper Detection, Less Manual Work, Happier Clients
Detection Accuracy
Precision jumped from 65% to 88%, giving the client far more reliable results for identifying unauthorized image use.
Limited Manual Review
Automated similarity scoring cut manual case analysis by 66%, freeing the team to focus on resolving confirmed infringements.
Faster Case Resolution
Faster, more accurate image matching reduced turnaround time, helping artists resolve infringement claims more quickly.
Extended Product Partnership
The client continued working with Maruti Techlabs as its product development partner, aligning on roadmap and ongoing service delivery.
Conclusion
The rebuilt engine gave the client's team faster, more reliable results, freeing up analyst time and deepening the long-term partnership.
Tech Stack
Build a computer vision engine trained on your image data to detect unauthorized use faster and more accurately.
Give Every Image a Fingerprint.
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Frequently Asked Questions
The model's core limitation was binary classification. It could only flag a match or non-match, not measure the actual degree of similarity, which required a fundamentally different architecture.
A similarity score lets the platform sort results into Exact Match, Similar Match, or No Match, giving artists more nuanced, trustworthy information about potential infringement.
It's a separate model trained specifically on logo datasets, added as a fifth layer to catch cases like watermark removal or logo placement that standard image matching would miss.
Photographers upload images from many different tools and devices, so supporting JPG, PNG, JPEG, TIFF, WEBP, GIF, and PDF was necessary to serve the full user base.
It validated whether visual similarity could be reliably measured using the client's image datasets and defined the technical approach before development began.