# gans Published articles for gans. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The GANfather: Using Malicious GenAI Agents to Combat Money Laundering DevFeed: [The GANfather: Using Malicious GenAI Agents to Combat Money Laundering]() Original publisher: [Read original article]() Author: Ricardo Ribeiro Pereira Published: 2024-10-04T13:49:42Z Content type: article Language: en Sources: [Feedzai]() Topics: [Generative AI](), [synthetic-data](), [Machine learning](), [AI Development](), [data]() Tags: [ai](), [banking](), [data](), [feedzai](), [financial-sector](), [gans](), [genai](), [generative](), [generative-ai](), [legacy](), [machine-learning](), [money-laundering](), [research](), [synthetic](), [synthetic-data](), [transactions]() ### AI overview Feedzai describes a method that uses Generative AI to create synthetic data simulating realistic money-laundering activity. The generated examples are intended to support machine-learning approaches to detection and help identify vulnerabilities in banks' defenses, addressing the limited availability of labeled data. ### Source excerpt Digital systems have become deeply integrated into many aspects of modern life, particularly within the financial sector. While digital banking simplifies day-to-day operations for clients, it also creates new opportunities for malicious actors to exploit these systems. As a result, money laundering has grown particularly prevalent due to this digital expansion. Banks are required to monitor for money laundering activities and issue alerts when suspicious transactions are detected. Typically, monitoring is performed by rules-based legacy systems. A better approach would be to use Machine Learning models, but these usually require labeled data to train, which are mostly unavailable in this use case. To tackle this problem, we employ advanced Generative AI (GenAI) techniques to generate synthetic data that simulates realistic money laundering activities. These synthetic examples help us identify vulnerabilities and strengthen the defense mechanisms used by banks and other financial institutions In this blog post, we will explore the method developed by Feedzai, which leverages GenAI to tackle the challenges of detecting and preventing money laundering in today's digital landscape. This blog post is the first of a series dedicated to the work done on GenAI by Feedzai Research in the last few years. Problem Statement First, let's briefly introduce the concepts behind money laundering and the difficulties that banks face when trying to prevent it. Money laundering is the process of concealing the origins of illegally obtained funds. Criminals cannot directly spend "dirty" money without risking exposure of their illegal activities. Therefore, they want to disguise the origins of funds before using them. Money laundering typically involves three stages: Placement: the money is introduced into the financial system, often in small amounts spread across various banks. Layering: the money launderer moves the funds through a series of transactions, typically across multiple fin ## MELON: Reconstructing 3D objects from images with unknown poses DevFeed: [MELON: Reconstructing 3D objects from images with unknown poses]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-03-18T18:41:00Z Content type: article Language: en Sources: [Google Research]() Topics: [3D](), [Computer vision](), [Inference](), [Google]() Tags: [3d](), [computer-vision](), [gans](), [gaussian-splatting](), [generative](), [google](), [images](), [inference](), [machine-learning](), [models](), [neural](), [research](), [rgb](), [rotation](), [techniques](), [vision]() ### AI overview This Google Research article explains the challenge of reconstructing 3D objects from a small number of images when the camera poses are unknown. It covers pose inference, pseudo-symmetries, local-minimum failures, and prior approaches including NeRF, 3D Gaussian Splatting, GAN-based methods, BARF, SAMURAI, GNeRF, VMRF, SparsePose, and RUST. ### Source excerpt Posted by Mark Matthews, Senior Software Engineer, and Dmitry Lagun, Research Scientist, Google Research A person's prior experience and understanding of the world generally enables them to easily infer what an object looks like in whole, even if only looking at a few 2D pictures of it. Yet the capacity for a computer to reconstruct the shape of an object in 3D given only a few images has remained a difficult algorithmic problem for years. This fundamental computer vision task has applications ranging from the creation of e-commerce 3D models to autonomous vehicle navigation. A key part of the problem is how to determine the exact positions from which images were taken, known as pose inference. If camera poses are known, a range of successful techniques -- such as neural radiance fields (NeRF) or 3D Gaussian Splatting -- can reconstruct an object in 3D. But if these poses are not available, then we face a difficult "chicken and egg" problem where we could determine the poses if we knew the 3D object, but we can't reconstruct the 3D object until we know the camera poses. The problem is made harder by pseudo-symmetries -- i.e., many objects look similar when viewed from different angles. For example, square objects like a chair tend to look similar every 90° rotation. Pseudo-symmetries of an object can be revealed by rendering it on a turntable from various angles and plotting its photometric self-similarity map. Self-Similarity map of a toy truck model. Left: The model is rendered on a turntable from various azimuthal angles, θ. Right: The average L2 RGB similarity of a rendering from θ with that of θ*. The pseudo-similarities are indicated by the dashed red lines. The diagram above only visualizes one dimension of rotation. It becomes even more complex (and difficult to visualize) when introducing more degrees of freedom. Pseudo-symmetries make the problem ill-posed, with naïve approaches often converging to local minima. In practice, such an approach might mistake the ## Limitations of a single AI model DevFeed: [Limitations of a single AI model]() Original publisher: [Read original article]() Author: Frank Fischer Published: 2023-08-07T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Artificial Intelligence](), [Large Language Model](), [Machine learning](), [Deep learning]() Tags: [acquisition](), [ai](), [application-security](), [artificial-intelligence](), [awareness](), [blog](), [code](), [deep-learning](), [devops](), [devsecops](), [executive](), [gans](), [llm](), [ml](), [pmm](), [security](), [snyk-code](), [snyk-platform]() ### AI overview The article explains that artificial intelligence is broader than large language models, describing logical and statistical approaches and giving cybersecurity examples such as intrusion detection and SAST. It also introduces generative AI, GANs, and Transformers. ### Source excerpt Let's discuss the limitations of relying on a single AI model and the pros and cons of today's most popular models.