A Photo Identification Framework to Prevent Copyright Infringement with Manipulations

Abstract

In recent years, copyright infringement has been one of the most serious problems that hamper the development of the culture and arts industry. Due to the limitations of existing image search services, these infringements have not been properly identified and the number of infringements has been increasing continuously. To uncover these infringements and handle big data extracted from copyright photos, we propose a photo copyright identification framework to accurately handle manipulations of stolen photos. From a collage of cropped photos, regions of interest (RoIs) are detected to reduce the influence of cropping and identify each photo by Image RoI Detection. Binary descriptors for quick database search are generated from the RoIs by Image Hashing robustly to geometric and color manipulations. The matching results of Image Hashing are verified by measuring their similarity using the proposed Image Verification to reduce false positives. Experimental results demonstrate that the proposed framework outperforms other image retrieval methods in identification accuracy and significantly reduces the false positive rate by 2.8%. This framework is expected to identify copyright infringements in practical situations and have a positive effect on the copyright market.

Methodological Approach for Identifying Websites with Infringing Content via Text Transformers and Dense Neural Networks

“…On the other hand, in the active identification of infringing sites, twenty-one [25,[28][29][30][31][32][33][34][35][64][65][66][67][68][69][70][71][72][73][74][75] significant research contributions were compiled. Among these, three [65][66][67] engage in non-technical actions such as manual search and reporting, contributing to the legal takedown of offending sites.…”

“…Six projects [28,29,[68][69][70][71] follow a technical route, involving surveillance and cyber patrolling within the IoE. Regrettably, only twelve initiatives [28][29][30][31][32][33][34][35][72][73][74][75] implement one or more AI/ML techniques for identifying infringing content on the IoE, and of these, only eight [28][29][30][31][32][33][34][35] are considered closely related studies. Notably, a single article [25] stands out for its purely technical approach to the defensive and active aspects.…”

“…Object/Character Recognition [32,74] It is a prototype technique that applies Optical Object and Character Recognition (OCR) to determine whether unauthorized advertisements, promotions, offerings, and sales of a multimedia file can be found within the visual content of the website in question. This involves exploring the DOM and converting each image to text for subsequent evaluation.…”

“…-Technical [28,29,[68][69][70][71] involves any technical procedure that allows for traversing the IoE to identify websites, links, URLs, P2P platforms, File Transfer Protocol (FTP) endpoints, torrents, and storage clouds where an identified infringing multimedia resource is residing. -AI/ML-driven [28][29][30][31][32][33][34][35][72][73][74][75] includes advanced schemes that harness the power of AI/ML algorithms to efficiently perform cyber patrolling by traversing the IoE using NLP techniques, Supervised Learning (SL), Non-Supervised Learning (NSL), and DL analyses to analyze, discover, and present potential infringing multimedia content.…”