JPEG Fake Media: a provenance-based sustainable approach to secure and trustworthy media annotation

Abstract

Media assets can easily be manipulated with photo editing software or artificially created using deep learning techniques. This can be done with the intention to mislead, but also for creative or educational purposes. Clear annotation of media modifications is a crucial element to assess trustworthiness. However, these annotations should be attached securly to prevent them from being compromised. In addition, to achieve a wide adoption, interoperability is essential. This paper gives an overview of the media manipulation history, discusses the state-of-the-art and challenges related to AI-based detection methods. The paper then introduces JPEG Fake Media as a provenance-based sustainable approach to secure and trustworthy media annotation. JPEG Fake Media has the objective to produce a standard that can facilitate secure and reliable annotation of media asset creation and modifications. The standard shall support good faith usage scenarios as well as those with malicious intent.