# Knowledge Sharing via Domain Adaptation in Customs Fraud Detection

arXiv: https://arxiv.org/abs/2201.06759 · HTML: https://mlsanity.com/paper/2201.06759/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 39 references that are in the map.
- First posted on arXiv 18 Jan 2022.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Contrastive Adaptation Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1901.00976/) (2019)
- [Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning](https://mlsanity.com/paper/2011.01403/) (2020)
- [Characterizing and Avoiding Negative Transfer](https://mlsanity.com/paper/1811.09751/) (2018)
- [A Survey on Negative Transfer](https://mlsanity.com/paper/2009.00909/) (2020)
- [Open Compound Domain Adaptation](https://mlsanity.com/paper/1909.03403/) (2019)
- [Discriminative Adversarial Domain Adaptation](https://mlsanity.com/paper/1911.12036/) (2019)
- [Unsupervised Scene Adaptation with Memory Regularization in vivo](https://mlsanity.com/paper/1912.11164/) (2019)
- [Outer Product-based Neural Collaborative Filtering](https://mlsanity.com/paper/1808.03912/) (2018)
- [Adaptive Consistency Regularization for Semi-Supervised Transfer Learning](https://mlsanity.com/paper/2103.02193/) (2021)
- [Contrastive Domain Adaptation](https://mlsanity.com/paper/2103.15566/) (2021)
- [Analyzing noise in autoencoders and deep networks](https://mlsanity.com/paper/1406.1831/) (2014)
- [Ranger21: a synergistic deep learning optimizer](https://mlsanity.com/paper/2106.13731/) (2021)
- [Progressive Memory Banks for Incremental Domain Adaptation](https://mlsanity.com/paper/1811.00239/) (2018)
- [Active Learning for Human-in-the-Loop Customs Inspection](https://mlsanity.com/paper/2010.14282/) (2020)
- [Customs Fraud Detection in the Presence of Concept Drift](https://mlsanity.com/paper/2109.14155/) (2021)

## Built on by (most cited first, 5 of 5)

- [FedX: Unsupervised Federated Learning with Cross Knowledge Distillation](https://mlsanity.com/paper/2207.09158/) (2022)
- [FedDefender: Client-Side Attack-Tolerant Federated Learning](https://mlsanity.com/paper/2307.09048/) (2023)
- [Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Field](https://mlsanity.com/paper/2212.04329/) (2022)
- [FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2404.11905/) (2024)
- [CleverCatch: A Knowledge-Guided Weak Supervision Model for Fraud Detection](https://mlsanity.com/paper/2510.13205/) (2025)

## Abstract (arXiv)

Knowledge of the changing traffic is critical in risk management. Customs offices worldwide have traditionally relied on local resources to accumulate knowledge and detect tax fraud. This naturally poses countries with weak infrastructure to become tax havens of potentially illicit trades. The current paper proposes DAS, a memory bank platform to facilitate knowledge sharing across multi-national customs administrations to support each other. We propose a domain adaptation method to share transferable knowledge of frauds as prototypes while safeguarding the local trade information. Data encompassing over 8 million import declarations have been used to test the feasibility of this new system, which shows that participating countries may benefit up to 2-11 times in fraud detection with the help of shared knowledge. We discuss implications for substantial tax revenue potential and strengthened policy against illicit trades.