Marco Schreyer
Marco Schreyer
Verified email at unisg.ch - Homepage
Title
Cited by
Cited by
Year
Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks
M Schreyer, T Sattarov, D Borth, A Dengel, B Reimer
arXiv preprint arXiv:1709.05254, 2017
442017
Evaluation of Graylevel-features for Printing Technique Classification in High-throughput Document Management Systems
C Schulze, M Schreyer, A Stahl, T Breuel
Computational Forensics, 35-46, 2008
302008
Using DCT Features for Printing Technique and Copy Detection
C Schulze, M Schreyer, A Stahl, T Breuel
Advances in Digital Forensics V, 95-106, 2009
182009
Intelligent Printing Technique Recognition and Photocopy Detection for Forensic Document Examination.
M Schreyer, C Schulze, A Stahl, W Effelsberg
Informatiktage 8, 39-42, 2009
162009
Automatic Counterfeit Protection System Code Classification
J Van Beusekom, M Schreyer, TM Breuel
Media Forensics and Security, 75410F, 2010
122010
Adversarial Learning of Deepfakes in Accounting
M Schreyer, T Sattarov, B Reimer, D Borth
NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness …, 2019
72019
Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks
M Schreyer, T Sattarov, C Schulze, B Reimer, D Borth
KDD 2019 Workshop on Anomaly Detection in Finance, 2019
42019
Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks
M Schreyer, T Sattarov, A Gierbl, B Reimer, D Borth
ACM International Conference on AI in Finance (ICAIF '20), 2020
2020
Künstliche Intelligenz in der Wirtschaftsprüfung-Identifikation ungewöhnlicher Buchungen in der Finanzbuchhaltung
M Schreyer, T Sattarov, D Borth, A Dengel, B Reimer
WPg-Die Wirtschaftsprüfung 72 (11), 674-681, 2018
2018
Visual Exploration of Journal Entries to Detect Accounting Irregularities and Fraud
A Tatu, M Schreyer, J Hagelauer, J Wang
IEEE Workshop business | VIS | 14, 2014
2014
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Articles 1–10