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.
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