Uncovering Patterns in Car Parts – A Step Towards Combating a Cybercrime

The black market for stolen car parts is a significant problem, exacerbated by the rise of online marketplaces like Craigslist or OfferUp, where stolen goods are often sold under the radar. In response to this growing issue, our research team at Baylor University has been leveraging cutting-edge AI techniques to detect patterns in car part sales that could signal illicit activity. This work is part of the NSF-funded Research Experiences for Undergraduates (REU) program, which provides undergraduate students with hands-on research experience in critical areas like artificial intelligence. Our project, supported by NSF Grant #2210091, investigates the potential of deep learning models to analyze vast amounts of data from online listings, offering a new tool in the fight against stolen car parts.

Why This Research Matters

The theft and resale of car parts not only affect vehicle owners but also contribute to organized crime. Detecting patterns in how stolen parts are sold online can help law enforcement track and dismantle these criminal networks. This project also presents a unique challenge to the AI research community: the complexity of analyzing unstructured, noisy data from real-world platforms. By utilizing the Vision Transformer (ViT) for image analysis, our research offers a different approach compared to previous works that employed multimodal models like ImageBind and OpenFlamingo.

Dataset and Embedding Extraction

Our dataset comprises thousands of car parts advertisements scraped from Craigslist and OfferUp, each including images and textual descriptions. To process the image data, we used the Vision Transformer (ViT), a model pre-trained on ImageNet-21k. ViT processes images by splitting them into 16×16-pixel patches, allowing for the extraction of key features from each image. These features were converted into embeddings—high-dimensional vectors that represent each image’s content in a form that the model can analyze.

We extracted embeddings for nearly 85,000 images, which were then compiled into a CSV file for further analysis, including clustering and visualization. Unlike prior works by Hamara & Rivas (2024) and Rashid & Rivas (2024), which utilized multimodal models like ImageBind and OpenFlamingo to fuse image and text data, we focused solely on image embeddings in this phase to assess the effectiveness of ViT in capturing visual patterns related to illicit activities.

Clustering and Evaluation

With the embeddings extracted, we used UMAP (Uniform Manifold Approximation and Projection) to project the high-dimensional data into a more interpretable 2D space for visualization. We then applied K-Means clustering, a widely used algorithm for grouping data, and experimented with different embedding dimensions—16, 32, 64, and 128—to identify the optimal number of clusters.

Among these, 64 dimensions proved to be the best suited for our dataset, as determined by three key clustering performance metrics:

Silhouette Score: Measures how similar an object is to its own cluster compared to other clusters. A value of 0.015 indicated that some clusters were poorly defined.
Calinski-Harabasz Index: Evaluates the variance ratio between clusters versus within clusters.
Davies-Bouldin Index: Measures the average similarity between each cluster and its most similar cluster.

Although 128 dimensions performed well in some tests, 64 dimensions provided the clearest balance between cluster purity and computational efficiency. The low silhouette score, while indicating some overlap between clusters, helped confirm that most clusters were well-defined, despite several outliers—posts that displayed mixed or unclear features, such as images showing both powertrains and vehicle exteriors.

Findings and Analysis

Using the K-Means algorithm, we identified 20 distinct clusters, each representing different categories of car parts. Here are some key findings:

Cluster 0: Primarily contained exterior shots of full vehicles.
Cluster 1: Featured exterior components like mirrors and bumpers.
Cluster 2: Focused on powertrain parts such as engines and transmissions.
Cluster 3: Showcased body panels including doors, trunks, and hoods.
Cluster 4: Grouped images of towing accessories like trailer hitches.

After clustering, we applied K-Nearest Neighbors (KNN) to identify the top 10 posts nearest to each cluster centroid, which allowed us to analyze representative posts and confirm the coherence of each cluster. Despite the general success of this approach, outliers emerged in the UMAP visualization, indicating the need for further refinement to handle posts with mixed features. This challenge is common in image analysis, particularly when models rely solely on visual data without the contextual information that multimodal models can provide.

Comparative Analysis with Prior Work

Our approach contrasts with that of Hamara & Rivas (2024) and Rashid & Rivas (2024), who utilized multimodal models like ImageBind and OpenFlamingo to integrate image and text data for enhanced analysis. While their methods leveraged the fusion of multiple data types to capture richer context, we aimed to assess the capabilities of ViT in isolating visual patterns indicative of illicit activity. This comparison highlights the trade-offs between focusing on single-modality models versus multimodal approaches in detecting complex patterns within unstructured data.

Broader Impact

This research demonstrates the potential of AI in analyzing large, unstructured datasets from online marketplaces, providing law enforcement with new tools to monitor and track stolen car parts. From a technical perspective, our project highlights the effectiveness of using ViT for image analysis in this context. As we continue refining our models and consider integrating multimodal approaches inspired by prior work, our collaboration with crosdisciplinary partners will ensure that this system becomes a valuable tool for combating the sale of stolen goods online.

As stated previously, the silhouette score for the dataset proved to be notably small, which was supported by the visualization containing numerous outliers. This may be attributed to clusters lacking clear definition, meaning that several posts contained images without many distinguishable features. This is understandable considering that while clusters emphasized a focus on specific car parts, many images still displayed various other vehicle components. For instance, although Cluster 2 primarily featured images of powertrains, the posts in this cluster also included shots of the exterior and body panels of the vehicle. This is logical as sellers often aim to showcase multiple facets of the vehicle when listing it, explaining the lack of focus on specific car parts.

About the Author

Cameron Armijo is a Computer Science undergraduate student at Baylor University, specializing in data mining.

NSF Award: Using NLP to Identify Suspicious Transactions in Omnichannel Online C2C Marketplaces

Baylor University has been awarded funding under the SaTC program for Enabling Interdisciplinary Collaboration; a grant led by Principal Investigator Dr. Pablo Rivas and an amazing group of multidisciplinary researchers formed by:

Dr. Gissella Bichler from California State University San Bernardino, Center for Criminal Justice Research, School of Criminology and Criminal Justice.
Dr. Tomas Cerny is at Baylor University in the Computer Science Department, leading software engineering research.
Dr. Laurie Giddens from the University of North Texas, a faculty member at the G. Brint Ryan College of Business.
Dr. Stacy Petter is at Wake Forest University in the School of Business. She and Dr. Giddens have extensive research and funding in human trafficking research.
Dr. Javier Turek, a Research Scientist in Machine Learning at Intel Labs, is our collaborator in matters related to machine learning for natural language processing.

We also have two Ph.D. students working on this project: Alejandro Rodriguez and Korn Sooksatra.

This project was motivated by the increasing pattern of people buying and selling goods and services directly from other people via online marketplaces. While many online marketplaces enable transactions among reputable buyers and sellers, some platforms are vulnerable to suspicious transactions. This project investigates whether it is possible to automate the detection of illegal goods or services within online marketplaces. First, the project team will analyze the text of online advertisements and marketplace policies to identify indicators of suspicious activity. Then, the team will adapt the findings to a specific context to locate stolen motor vehicle parts advertised via online marketplaces. Together, the work will lead to general ways to identify signals of illegal online sales that can be used to help people choose trustworthy marketplaces and avoid illicit actors. This project will also provide law enforcement agencies and online marketplaces with insights to gather evidence on illicit goods or services on those marketplaces.

This research assesses the feasibility of modeling illegal activity in online consumer-to-consumer (C2C) platforms, using platform characteristics, seller profiles, and advertisements to prioritize investigations using actionable intelligence extracted from open-source information. The project is organized around three main steps. First, the research team will combine knowledge from computer science, criminology, and information systems to analyze online marketplace technology platform policies and identify platform features, policies, and terms of service that make platforms more vulnerable to criminal activity. Second, building on the understanding of platform vulnerabilities developed in the first step, the researchers will generate and train deep learning-based language models to detect illicit online commerce. Finally, to assess the generalizability of the identified markers, the investigators will apply the models to markets for motor vehicle parts, a licit marketplace that sometimes includes sellers offering stolen goods. This project establishes a cross-disciplinary partnership among a diverse group of researchers from different institutions and academic disciplines with collaborators from law enforcement and industry to develop practical, actionable insights.

Self-supervised modeling. After providing a corpus associated with a C2C domain of interest and ontologies, we will extract features followed by attention mechanisms for self-supervised and supervised tasks. The self-supervised models include the completion of missing information and domain-specific text encoding for learning representations. Then supervised tasks will leverage these representations to learn the relationships with targets.

Hybrid Quantum Variational Autoencoders for Representation Learning

One of our recent papers introduces a novel hybrid quantum machine learning approach to unsupervised representation learning by using a quantum variational circuit that is trainable with traditional gradient descent techniques. Access it here: [ bib | .pdf ]

Much of the work related to quantum machine learning has been popularized in recent years. Some of the most notable efforts involve variational approaches (Cerezo 2021, Khoshaman 2018, Yuan 2019). Researchers have shown that these models are effective in complex tasks that grant further studies and open new doors for applied quantum machine learning research. Another popular approach is to perform kernel learning using a quantum approach (Blank 2020, Schuld 2019, Rebentrost 2014). In this case the kernel-based projection of data $\mathbf{x}$ produces a easible linear mapping to the desired target $y$ as follows:

(1) $\begin{equation*} y(\mathbf{x})=\operatorname{sign}\left(\sum_{j=1}^{M} \alpha_{j} k\left(\mathbf{x}_{j}, \mathbf{x}\right)+b\right) \end{equation*}$

for hyper parameters $b,\alpha$ that need to be provided or learned. This enables the creation of some types of support vector machines whose kernels are calculated such that the data $\mathbf{x}$ is processed in the quantum realm. That is $\left|\mathbf{x}_{j}\right\rangle=1 /\left|\mathbf{x}_{j}\right| \sum_{k=1}^{N}\left(\mathbf{x}_{j}\right)_{k}|k\rangle$ . The work of Schuld et al., expands the theory behind this idea an show that all kernel methods can be quantum machine learning methods. Recently, in 2020, Mari et al., worked on variational models that are hybrid in format. Particularly, the authors focused on transfer learning, i.e., the idea of bringing a pre-trained model (or a piece of it) to be part of another model. In the case of Mari the larger model is a computer vision model, e.g., ResNet, which is part of a variational quantum circuit that performs classification. The work we present here follows a similar idea, but we focus in the autoencoder architecture, rather than a classification model, and we focus on learning representations in comparison between a classic and a variational quantum fine-tuned model.