Deep Learning in Image Forensics (DLinIF)10 ECTS
(englische Bezeichnung: Deep Learning in Image Forensics)
(Prüfungsordnungsmodul: Projekt Modul)

Lehrveranstaltungen:

• • Deep Learning in Image Forensics
    (Praktikum, 2 SWS, Luisa Verdoliva et al., Zeit und Raum n.V.)

Die allgemeine Modulbeschreibung des Prüfungsordnungsmoduls Projekt Modul finden Sie hier.

Empfohlene Voraussetzungen:

Participants of this class are expected to bring a working knowledge in python. Also, participants are expected to bring theoretical and practical knowledge from classes in either pattern recognition, machine learning, or computer graphics.

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:

• weeks of May 7 until May 18: introductory lectures on image forensics and the tensorflow framework.

• weeks of May 21 until June 22: introductory project assignment

• weeks of 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

Lernziele und Kompetenzen:

Anwenden

Participants implement and train an existing deep learning architecture

Analysieren

Participants explore weaknesses of the trained network on provided benchmark data

Evaluieren (Beurteilen)

Participants validate their solution using common quality criteria in the field of image forensics

Erschaffen

Participants create and implement strategies to improve their developed solution against selected failure cases.

Bemerkung:

Please register to this class via email to Christian Riess.

Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:

1. Informatik (Master of Science)
   (Po-Vers. 2010 | TechFak | Informatik (Master of Science) | Seminar, Projekt, Masterarbeit | Projekt Modul)

Studien-/Prüfungsleistungen:

Deep Learning in Image Forensics (Prüfungsnummer: 922926)

(englischer Titel: Deep Learning in Image Forensics)

Prüfungsleistung, mehrteilige Prüfung, benotet, 10.0 ECTS

Anteil an der Berechnung der Modulnote: 100.0 %

weitere Erläuterungen:
The grade consists of: 50% implementation of software for the predefined task, 25% presentation of the software to the supervisors 25% compact description of the software and the results in a written text.

Prüfungssprache: Englisch

Erstablegung: SS 2018, 1. Wdh.: WS 2018/2019

1. Prüfer:

Christian Riess,

2. Prüfer:

Marc Stamminger