As during the last decade the limit between professional and personal usage of smartphones gradually disappeared, the present study is devoted to the tracking of the visual documents scanned by a personal mobile phone for some professional reasons. By a visual document we assume a composition of text, graphics and images corresponding to various physical-world documents (invoices, calls for tenders, legal contracts, etc.). As the scanning (capturing) conditions cannot be reproductible, the main issue is to unambiguously and securely identify various digital representations for a same physical document. A second issue is related to the inherent constraints in resources made available for such a task in the mobile/embedded environment. To jointly solve these issues, a solution based on coupling the blockchain technologies to the visual fingerprinting principles is advanced. The novel elements thus brought to light relate to (1) the coupling of fingerprint and blockchain solutions, (2) the unitary smart contracts generation and management (with illustrations for the Tezos blockchain) and (3) an on-chain / off-chain work balancing solution for coping to the mobile world constraints. The experimental results obtained on a database of more than 10 000 visual documents resulted in F1 score equal up to 0.98 while being compatible with low-resources computing environments (Raspberry Pi).
This paper describes how watermarking technology can be used to prevent the proliferation of Deepfake news. In the proposed system, digital watermarks are embedded in the audio and video tracks of video clips of trusted news agencies at the time the videos are captured or before they are distributed. The watermarks are detected at the social media network’s portals, nodes, and back ends. The embedded watermark imparts a unique identifier to the video, that links it to a blockchain. The watermarks also allow video source tracking, integrity verification, and alteration localization. The watermark detectors can be standalone software applications, or they can be integrated with other applications. They are used to perform three main tasks: (1) they alert the internet user when he watches an inauthentic news video, so that he may discard it, (2) they prevent a Deepfake news video from propagating through the network (3) they perform forensic analysis to help track and remove Deepfake news video postings. The paper includes Proof-of- Concept simulation results.