IFP (Image Fingerprint Protection) is a structural content identity and forensic license tracing system. It solves a problem that traditional reverse image search cannot: detecting unauthorized use of images after heavy visual modifications.
When metadata is stripped and the image is rotated, cropped, blurred, color-shifted, overlaid with UI elements, or all of the above — IFP re-links the modified version back to the registered original through structural fingerprinting.
IFP is an independent research initiative, not a company. It exists because the problem of proving image identity after intentional modification has no working solution in the current market — and the author decided to build one.
IFP is built on a proprietary algorithm for structural visual information indexing. It does not use AI or machine learning — it is a deterministic, code-based approach.
Traditional reverse image search (Google, TinEye, Yandex) uses perceptual similarity — finding images that look alike. This breaks down when the image is significantly modified.
IFP uses structural identity — proving that two images contain the same content, regardless of visual transformations applied. This means:
Existing solutions were designed to survive basic modifications — resizing, recompression, minor color shifts. They were not designed for adversarial modifications — intentional transformations meant to evade detection.
The gap between “finding similar images” and “proving this is the same image” is where IFP operates. C2PA and Content Credentials verify the origin of the file. IFP verifies the origin of the content. When metadata is gone, IFP still works — through the image structure itself.
Jevgeni Striganov is an independent researcher and systems architect with 15 years of applied experience across SaaS, enterprise, and AI products — including CRM platforms (Pipedrive), digital fingerprint masking systems (Multilogin), AI-based analytics, and complex multi-domain product environments (Bitsgap, Cointraffic, and several AI startups). Background in product design, but the kind that goes deep: forensic UX audits, data architecture analysis, domain-level product diagnostics.
IFP was developed through independent R&D combining cross-domain pattern recognition methodology with novel applications of established mathematical frameworks. It came from understanding how image identity systems work at the algorithmic level, and a drive to solve problems that sit at the edge of science and chaos — by structuring them with logic.
Not a computer vision researcher by training. A product thinker who saw a structural gap in how digital content ownership works — and built the algorithm to close it. Prior work includes UX architecture for technically complex products, AI product prototyping, and automated development pipeline engineering.
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