The proliferation of multimedia data, including digital images and videos, has led to an increase in their misuse, such as the unauthorized sharing of sensitive content, the spread of fake news, and the dissemination of misleading propaganda. To address these issues, the research field of multimedia forensics has developed tools to distinguish genuine multimedia from fakes and identify the sources of those who share sensitive content. However, the accuracy and reliability of multimedia forensics tools are threatened by recent technological advancements in new multimedia processing software and camera devices. For example, source attribution involves attributing an image or video to a specific camera device, which is crucial for addressing privacy violations, cases of revenge porn, and instances of child pornography. These tools exploit forensic traces unique to each camera’s manufacturing process, such as Photo Response Non-Uniformity (PRNU). Nevertheless, image and video processing transformations can disrupt the consistency of PRNU, necessitating the development of new methods for its recovery. Conversely, to distinguish genuine multimedia from fakes, AI-based image and video forgery localization methods have also emerged. However, they constantly face challenges from new, more sophisticated AI-forgery techniques and are hindered by factors like AI-aided post-processing and, in the case of videos, lower resolutions, and stronger compression. This doctoral study investigates the relevance of exploiting temporal information during the parameters estimation used to reverse complex spatial transformations for source attribution, and video forgery localization in low-resolution H.264 post-processed inpainted videos. Two novel methods will be presented that model the set of parameters involved in reversing in-camera and out-camera complex spatial transformations applied to images and videos as time series, improving source attribution accuracy and computational efficiency. Regarding video inpainting localization, a novel dataset of videos inpainted and post-processed with Temporal Consistency Networks will be introduced, and we will present our solution to improve video inpainting localization by taking into account spatial and temporal inconsistencies at dense optical flow level. The research presented in this dissertation has resulted in several publications that contribute to the field of multimedia forensics, addressing challenges related to source attribution and video forgery localization.

Morphing, ein Spezialeffekt zur Generierung eines Übergangs von einem Bild zum anderen, hat seinen Ursprung in der Filmindustrie, kann aber auch für kriminelle Zwecke missbraucht werden. Ein Zwischenbild eines Morphs, der das Gesicht einer Person in das einer anderen Person überführt, ähnelt beiden Gesichtern. Wenn ein solches Bild für einen Ausweis oder Reisepass verwendet wird, können beide behaupten, dessen Eigentümer zu sein. So könnten sich beide beispielsweise ein personengebundenes Verkehrsticket teilen oder es könnten illegal und unbemerkt Ländergrenzen überquert werden. Diese Dissertation stellt neue, auf neuronalen Netzen basierende Methoden zur Erkennung von Gesichtsmorphs und zur Lokalisierung von Fälschungsspuren vor. In Experimenten mit teilweise gemorphten Bildern wird gezeigt, dass die vorgestellten Detektoren in Kombination mit der vorgestellten Erklärbarkeitsmethode wesentlich genauer Fälschungsspuren lokalisieren können als andere gängige Methoden. Zum Trainieren der in der Arbeit entwickelten Detektoren wird eine große Menge an repräsentativen Daten benötigt. Daher legt diese Dissertation einen Schwerpunkt auf die automatische Erstellung von Gesichtsmorphs. Dazu stellt sie zwei Methoden vor, die Artefakte, die durch den Registrierungs- und Überblendungsschritt beim Morphing entstehen, deutlich reduzieren oder sogar vermeiden. Beide Verbesserungsmethoden ahmen die Möglichkeiten nach, die ein Angreifer durch manuelle Anpassungen hat. Die vorgestellten Detektoren wurden auf internen und externen Datensätzen evaluiert. Zusätzlich wurde ein Detektor bei einem international anerkannten Benchmark eingereicht. Dabei übertraf dieser andere Einreichungen in mehreren Kategorien deutlich. Zusammenfassend stellt diese Arbeit einen robusten und transparenten Detektor für gemorphte Gesichtsbilder vor, der Fälschungsspuren akkurat lokalisiert, mit dem Ziel einer nachvollziehbareren Klassifikation, sowie neue Methoden zur Erstellung von hochwertigen Gesichtsmorphs. / Morphing, as a smooth transformation of one image into another, originated in the cinematic industry. Beyond its entertainment applications, it can also be used for malicious purposes. An intermediate step of the morph from one person's face to that of a different one results in a synthetic face image that resembles both persons. If such an image is used for an ID card or passport, two individuals could claim ownership and share the associated privileges. Consequences can range from sharing a personal ticket for public transportation to entering a country unnoticed and without permission. This dissertation introduces novel methods for detecting morphed face images using Deep Neural Networks and proposes approaches to precisely identify traces of forgery. Experiments with partially morphed face images show that the proposed detection approaches in combination with this explainability method outperform other methods. A prerequisite for developing machine learning-based detectors is to have a substantial amount of representative data. Therefore, this thesis also emphasizes the automatic generation of morphed images and proposes two methods that mitigate artifacts caused by the alignment and blending step of the face morphing process. These improvement methods mimic the capabilities an attacker has through manual adjustments. The proposed detectors are evaluated on internal and on external datasets. Additionally, a proposed detector was submitted to an internationally renowned challenge. In this external benchmark, the submitted detector significantly outperforms other state-of-the-art submissions across multiple categories. As a summary, this thesis introduces a robust and transparent face morphing detector that is capable of highlighting detected traces of forgery to support humans in understanding the detector's decision, as well as advanced methods to improve the automatic generation of morphed face images.

The widespread availability of image editing tools and improvements in image processing techniques make image manipulation feasible for the general population. Oftentimes, easy-to-use yet sophisticated image editing tools produce results that contain modifications imperceptible to the human observer. Distribution of forged images can have drastic ramifications, especially when coupled with the speed and vastness of the Internet. Therefore, verifying image integrity poses an immense and important challenge to the digital forensic community. Satellite images specifically can be modified in a number of ways, such as inserting objects into an image to hide existing scenes and structures. In this thesis, we describe the use of a Conditional Generative Adversarial Network (cGAN) to identify the presence of such spliced forgeries within satellite images. Additionally, we identify their locations and shapes. Trained on pristine and falsified images, our method achieves high success on these detection and localization objectives.