A Web-Based Service for Disturbing Image Detection
As User Generated Content takes up an increasing share of the total Internet multimedia traffic, it becomes increasingly important to protect users (be they consumers or professionals, such as journalists) from potentially traumatizing content that is accessible on the web. In this demonstration, we present a web service that can identify disturbing or graphic content in images. The service can be used by platforms for filtering or to warn users prior to exposing them to such content. We evaluate the performance of the service and propose solutions towards extending the training dataset and thus further improving the performance of the service, while minimizing emotional distress to human annotators.
KeywordsConvolutional Neural Network Transfer Learning User Generate Content Rest Service Convolutional Neural Network Model
This work is supported by the REVEAL and InVID projects, partially funded by the European Commission under contract numbers 610928 and 687786. In addition, we would like to acknowledge the support that NVIDIA provided us through the GPU Grant Program.
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