TRUSTD: Combat Fake Content using Blockchain and Collective Signature Technologies

Abstract

The growing trend of sharing news/contents, through social media platforms and the World Wide Web has been seen to impact our perception of the truth, altering our views about politics, economics, relationships, needs and wants. This is because of the growing spread of misinformation and disinformation intentionally or unintentionally by individuals and organizations. This trend has grave political, social, ethical, and privacy implications for society due to 1) the rapid developments in the field of Machine Learning (ML) and Deep Learning (DL) algorithms in creating realistic-looking yet fake digital content (such as text, images, and videos), 2) the ability to customize the content feeds and to create a polarized so-called "filterbubbles" leveraging the availability of the big-data. Therefore, there is an ethical need to combat the flow of fake content. This paper attempts to resolves some of the aspects of this combat by presenting a high-level overview of TRUSTD, a blockchain and collective signature based ecosystem to help content creators in getting their content backed by the community, and to help users judge on the credibility and correctness of these contents.

The Application of Blockchain in Social Media: A Systematic Literature Review

“…Proposed Model [46] Blockchain and keyed watermarking-based framework [47] Architecture of blockchain-based fictitious detection system [48] Architecture to store validation records in the blockchain [49] Rating system to detect the authenticity of news [50] A secure and privacy-preserving method [51] A framework for safely sharing information at the peer level [52] Machine Learning-based model using Blockchain [56] AI with a Proof-of-Stake smart contract algorithm architecture [57] Blockchain with Text Mining (TM) algorithm [58] Social media news-spreading model [61] A blockchain and watermarking-based social media framework [63] TRUSTD, a blockchain and collective signature-based ecosystem [64] A smart contract-based technique to avert fake posts [66] WhistleBlower, a decentralized fake news detection platform [67] Proof of Trustworthiness (PoT) [69] Avoiding false check-ins in Location-Based Social Networks [72] Method using the concept of decentralization [73] A blockchain-based social media notarization method [74] A blockchain consensus called Proof of Credibility (PoC) [75] A public opinion communication model [77] Architecture using data mining as a consensus algorithm [78] A prototype to counter the dissemination of fake news [79] Proof of Truthfulness (PoT) for news verification [85] Provenator, a blockchain-based distributed application…”

“…They deployed this BDN through bloXroute servers to obtain a scalable system. Zakwan Jaroucheh et al [63] suggest TRUSTD, an environment permitting a user to evaluate an element's accuracy and reliability. Shovon Gengxin Sun et al [75] proposed a public opinion dissemination system for a social network based on Blockchain.…”

Secure Face and Liveness Detection with Criminal Identification for Security Systems

“…3, we suggest using the adaptive fusion of convolutional features from real-world face pictures and deep CNN-based auto-encoder generated (DNG) face images. For the purpose of detecting the liveness of a face, we specifically use the adaptive disparity and blending between neural features of real-world face pictures and DNG face images learnt by convolutional layers with shared weights in CNN [7][8][9][10]. As shown in Fig.…”