Beyond Static Datasets: A Behavior-Driven Entity-Specific Simulation to Overcome Data Scarcity and Train Effective Crypto Anti-Money Laundering Models

For different factors/reasons, ranging from inherent characteristics and features providing decentralization, enhanced privacy, ease of transactions, etc., to implied external hardships in enforcing regulations, contradictions in data sharing policies, etc., cryptocurrencies have been severely abused for carrying out numerous malicious and illicit activities including money laundering, darknet transactions, scams, terrorism financing, arm trades. However, money laundering is a key crime to be mitigated to also suspend the movement of funds from other illicit activities. Billions of dollars are annually being laundered. It is getting extremely difficult to identify money laundering in crypto transactions owing to many layering strategies available today, and rapidly evolving tactics, and patterns the launderers use to obfuscate the illicit funds. Many detection methods have been proposed ranging from naive approaches involving complete manual investigation to machine learning models. However, there are very limited datasets available for effectively training machine learning models. Also, the existing datasets are static and class-imbalanced, posing challenges for scalability and suitability to specific scenarios, due to lack of customization to varying requirements. This has been a persistent challenge in literature. In this paper, we propose behavior embedded entity-specific money laundering-like transaction simulation that helps in generating various transaction types and models the transactions embedding the behavior of several entities observed in this space. The paper discusses the design and architecture of the simulator, a custom dataset we generated using the simulator, and the performance of models trained on this synthetic data in detecting real addresses involved in money laundering.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization

Pioneering advancements in artificial intelligence, especially in genAI, have enabled significant possibilities for content creation, but also led to widespread misinformation and false content. The growing sophistication and realism of deepfakes is raising concerns about privacy invasion, identity theft, and has societal, business impacts, including reputational damage and financial loss. Many deepfake detectors have been developed to tackle this problem. Nevertheless, as for every AI model, the deepfake detectors face the wrath of lack of considerable generalization to unseen scenarios and cross-domain deepfakes. Besides, adversarial robustness is another critical challenge, as detectors drastically underperform to the slightest imperceptible change. Most state-of-the-art detectors are trained on static datasets and lack the ability to adapt to emerging deepfake attack trends. These three crucial challenges though hold paramount importance for reliability in practise, particularly in the deepfake domain, are also the problems with any other AI application. This paper proposes an adversarial meta-learning algorithm using task-specific adaptive sample synthesis and consistency regularization, in a refinement phase. By focussing on the classifier's strengths and weaknesses, it boosts both robustness and generalization of the model. Additionally, the paper introduces a hierarchical multi-agent retrieval-augmented generation workflow with a sample synthesis module to dynamically adapt the model to new data trends by generating custom deepfake samples. The paper further presents a framework integrating the meta-learning algorithm with the hierarchical multi-agent workflow, offering a holistic solution for enhancing generalization, robustness, and adaptability. Experimental results demonstrate the model's consistent performance across various datasets, outperforming the models in comparison.