Conference Paper

Advances in deep neural networks (DNN) and distributed ledger technology (DLT) have shown major influence on
media security, authenticity and privacy. Current deepfake techniques can produce near realistic media content
which can be used in both good and bad intended use cases. At the same time, DLTs are finding their way in the
industry as fair, transparent and reliable means for content distribution. In particular non-fungible tokens (NFTs)
are emerging in the digital art market. However, such new developments also introduce new challenges, including
the need for robust and reliable metadata, a mechanism to secure the media and associated metadata, means to
verify authenticity and interoperability between various stakeholders. This paper identifies emerging challenges
in fake media and NFT, and proposes a novel framework to effectively cope with secure media applications
allowing for a structured, systematic, and interoperable solution. The framework relies on an architecture that
is modular, flexible, extensible, and scalable in the sense that it can be implemented in both lighter as well as
more feature-rich and more complex configurations depending on the underlying application, needed features and
available resources, while enabling products and services in various ecosystems with desired trust and security
capabilities. The framework is inspired by activities and developments within JPEG standardisation related to
security, authenticity & privacy.