Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the features as either a source or target and train a feature generator network to mimic the discriminator. Two problems exist with these methods. First, the domain classifier only tries to distinguish the features as a source or target and thus does not consider task-specific decision boundaries between classes. Therefore, a trained generator can generate ambiguous features near class boundaries. Second, these methods aim to completely match the feature distributions between different domains, which is difficult because of each domain's characteristics. To solve these problems, we introduce a new approach that attempts to align distributions of source and target by utilizing the task-specific decision boundaries. We propose to maximize the discrepancy between two classifiers' outputs to detect target samples that are far from the support of the source. A feature generator learns to generate target features near the support to minimize the discrepancy. Our method outperforms other methods on several datasets of image classification and semantic segmentation. The codes are available at \url{https://github.com/mil-tokyo/MCD_DA}Comment: Accepted to CVPR2018 Oral, Code is available at https://github.com/mil-tokyo/MCD_D

How the Kremlin continued its social media influence campaign in the United States in 2020

Ahead of the 2020 presidential election, there was much concern over the likelihood that Russia would try to influence the vote, much as it did in 2016. In new research, Maria Snegovaya and Kohei Watanabe explore the Kremlin’s recent social media influence campaigns in the United States by analyzing the effectiveness of Russia’s information operations and the susceptibility of specific [...

VELLET: Verifiable Embedded Wallet for Securing Authenticity and Integrity

The blockchain ecosystem, particularly with the rise of Web3 and Non-Fungible Tokens (NFTs), has experienced a significant increase in users and applications. However, this expansion is challenged by the need to connect early adopters with a wider user base. A notable difficulty in this process is the complex interfaces of blockchain wallets, which can be daunting for those familiar with traditional payment methods. To address this issue, the category of "embedded wallets" has emerged as a promising solution. These wallets are seamlessly integrated into the front-end of decentralized applications (Dapps), simplifying the onboarding process for users and making access more widely available. However, our insights indicate that this simplification introduces a trade-off between ease of use and security. Embedded wallets lack transparency and auditability, leading to obscured transactions by the front end and a pronounced risk of fraud and phishing attacks. This paper proposes a new protocol to enhance the security of embedded wallets. Our VELLET protocol introduces a wallet verifier that can match the audit trail of embedded wallets on smart contracts, incorporating a process to verify authenticity and integrity. In the implementation architecture of the VELLET protocol, we suggest using the Text Record feature of the Ethereum Name Service (ENS), known as a decentralized domain name service, to serve as a repository for managing the audit trails of smart contracts. This approach has been demonstrated to reduce the necessity for new smart contract development and operational costs, proving cost-effective through a proof-of-concept. This protocol is a vital step in reducing security risks associated with embedded wallets, ensuring their convenience does not undermine user security and trust.Comment: A shortened version is to be published at the IEEE International Conference on Blockchain and Cryptocurrency (ICBC) 202