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Vorlesungsverzeichnis >> Technische Fakultät (TF) >>
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Deep Learning in Image Forensics (DLinIF)- Dozentinnen/Dozenten
- Prof. Dr. Luisa Verdoliva, Dr.-Ing. Christian Riess
- Angaben
- Praktikum
2 SWS, benoteter Schein, ECTS-Studium, ECTS-Credits: 10, Sprache Englisch
Zeit und Ort: n.V.
- Studienfächer / Studienrichtungen
- WPF INF-MA 1 (ECTS-Credits: 10)
- Voraussetzungen / Organisatorisches
- Please send an email to Christian Riess for registration to this class.
- Inhalt
- Is an image pristine, or has its content been edited? Manipulation detection is one of the goals in image forensics. In this hands-on class, we will look at the currently most popular learning-based approaches to manipulation detection. A particular focus of this class will lie on the task of training a deep neural network for image manipulation detection. The topic will be introduced in a few lectures. Then, the participants will experiment with an own implementation of a neural network for manipulation detection. The network training and performance assessment will be done on provided benchmark data. In the course of the semester, weaknesses in the network performance will be analyzed, and based on this analysis, the network will be gradually improved.
Tentative semester outline:
May 7 until May 18: introductory lectures on image forensics and the tensorflow framework. May 21 until June 22: introductory project assignment June 25 until July 6: advanced lectures on deep learning in image forensics July 9 until September 10: main project on image manipulation detection using deep learning
- ECTS-Informationen:
- Credits: 10
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 10, Maximale Teilnehmerzahl: 10
- Verwendung in folgenden UnivIS-Modulen
- Startsemester SS 2018:
- Deep Learning in Image Forensics (DLinIF)
- Institution: Lehrstuhl für Informatik 9 (Graphische Datenverarbeitung)
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