Evaluation of Graylevel-Features for Printing Technique Classification in High-Throughput Document Management Systems. - Christian Schulze 001; Marco Schreyer; Armin Stahl; Thomas M. Breuel (IWCF, 2008)

Using DCT Features for Printing Technique and Copy Detection. - Christian Schulze 001; Marco Schreyer; Armin Stahl; Thomas M. Breuel (IFIP Int. Conf. Digital Forensics, 2009)

Intelligent Printing Technique Recognition and Photocopy Detection for Forensic Document Examination. - Marco Schreyer (Informatiktage, 2009)

Automatic counterfeit protection system code classification. - Joost van Beusekom; Marco Schreyer; Thomas M. Breuel (Media Forensics and Security, 2010)

Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks. - Marco Schreyer; Timur Sattarov; Damian Borth; Andreas Dengel 001; Bernd Reimer (CoRR, 2017)

Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks. - Marco Schreyer; Timur Sattarov; Christian Schulze 001; Bernd Reimer; Damian Borth (CoRR, 2019)

Adversarial Learning of Deepfakes in Accounting. - Marco Schreyer; Timur Sattarov; Bernd Reimer; Damian Borth (CoRR, 2019)

Learning sampling in financial statement audits using vector quantised variational autoencoder neural networks. - Marco Schreyer; Timur Sattarov; Anita Gierbl; Bernd Reimer; Damian Borth (ICAIF, 2020)

Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks. - Marco Schreyer; Timur Sattarov; Anita Gierbl; Bernd Reimer; Damian Borth (CoRR, 2020)

Leaking Sensitive Financial Accounting Data in Plain Sight using Deep Autoencoder Neural Networks. - Marco Schreyer; Christian Schulze 001; Damian Borth (CoRR, 2020)

Multi-view contrastive self-supervised learning of accounting data representations for downstream audit tasks. - Marco Schreyer; Timur Sattarov; Damian Borth (ICAIF, 2021)

Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data. - Hamed Hemati; Marco Schreyer; Damian Borth (CoRR, 2021)

RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations. - Ricardo Müller; Marco Schreyer; Timur Sattarov; Damian Borth (ICAIF, 2022)

Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits. - Marco Schreyer; Timur Sattarov; Damian Borth (ICAIF, 2022)

Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing. - Marco Schreyer; Hamed Hemati; Damian Borth; Miklos A. Vasarhelyi (CoRR, 2022)

FinDiff: Diffusion Models for Financial Tabular Data Generation. - Timur Sattarov; Marco Schreyer; Damian Borth (ICAIF, 2023)

Imb-FinDiff: Conditional Diffusion Models for Class Imbalance Synthesis of Financial Tabular Data. - Marco Schreyer; Timur Sattarov; Alexander Sim; Kesheng Wu (ICAIF, 2024)

FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation. - Timur Sattarov; Marco Schreyer; Damian Borth (CoRR, 2024)

GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs. - Kristófer Reynisson; Marco Schreyer; Damian Borth (CoRR, 2024)

Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation. - Timur Sattarov; Marco Schreyer; Damian Borth (CoRR, 2024)

Artificial intelligence co-piloted auditing. - Hanchi Gu; Marco Schreyer; Kevin C. Moffitt; Miklos A. Vasarhelyi (Int. J. Account. Inf. Syst., 2024)

Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data. - Timur Sattarov; Marco Schreyer; Damian Borth (CoRR, 2025)