Core Concepts
Proposing a framework to determine the likelihood of an image being fake based on changes in image metadata.
Abstract
The article proposes a novel framework to assess the trustworthiness of crowdsourced images by focusing on changes in non-functional attributes. It introduces the concept of intention as a key parameter to ascertain fake images. The framework utilizes semantic analysis and clustering to estimate intention and translate it into fakeness. Experiments show high accuracy using real datasets.
Stats
"Our experiments show high accuracy using a large real dataset."
"It achieves 80-95% accuracy on a systematic set of experiments."