It is now possible to digitally capture the physical world around us or to synthesize realistic virtual contents and reproduce them in ways only imagined in science fiction a few decades ago. Digital information in form of audio and visual information is of increased fidelity in its quality and richer in information. Progress has been made towards representations of content that not only allow for immersive audio visual representations but also enhance the latter by other sensory information such as haptics, enabling an unprecedented user experience where the boundary between real and virtual becomes indistinguishable. Along with its undeniable advantages, digital information also brings its share of new challenges notably in a wide range of trust and security issues.
In this application vertical we address two main challenges in digital information: 1) adaptation of generic trust and security tools and architectures to efficiently apply them to digital information problems, 2) design, implementation and validation of new trust and security tools and architectures that best address problems specific to digital information that do not have a counterpart in generic information security and trust.
An example of the first challenge is in the application of blockchain and distributed ledger technologies for digital rights management (DRM) in speech, audio, image and video content. Examples for the second challenge are the detection of image forgery using photo editing tools to fight against malicious manipulations and the use of domain-specific image classification tools for filtering undesired contents (e.g. child protection, fake news).
Specific applications addressed in this vertical include: protection of digital information ownership (copyright), trusted news, biometrics, robust media integrity verification, conditional access, digital information forensics, privacy protection, domain specific media detection, tracking and monitoring, video surveillance, security in social media, steganography, 2D/3D print security, combined image/text classification.