The Internet today is experiencing a large growth in the amount of traffic due to the number of users consuming streaming media. For both the operator and content providers, streaming of media generates most of its revenue through advertisements inserted in the content. One common approach is to pre-stitched (i.e. insert) advertisements into the content. Another approach is dynamic advertisement insertion, which inserts advertisements at run-time while the media is being streamed. Dynamic advertisement insertion gives operators the flexibility to insert advertisements based on context, such as the user's geographic location or the user's preferences. Developing a technique to successfully insert advertisements dynamically into the streaming media has several challenges, such as maintaining synchronization of the media, choosing the appropriate transport format for media delivery, and finding a splicing boundary that starts with a key frame. The details of these challenges are detailed in this thesis. We carried out extensive research to find the best transport format for delivery of media and we studied prior work in an effort to find an appropriate streaming solution to perform dynamic advertisement insertion. Based upon this research and our study of prior work we identify the best transport format for delivery of media chunks, then propose, implement, and evaluate a technique for advertisement insertion. / Idag har internet mycket trafik på grund av att alltfler servrar erbjuder högkvalitativa videon som strömmas till internetanvändare. Både för operatörer och leverantörer av sådan innehåll genererar direktuppspelning mest intäkter genom annonser som lagts till i videon. Det är väldigt vanligt att lägga till annonser i videon genom att sy in dem i videofiler. En annan metod är att lägga till annonser dynamiskt. Det betyder att resulterande videofilen genereras medan den blir strömmad till användare. Att sätta in annonser dynamiskt har som fördel för operatörer att välja reklam beroende på kontexten, såsom användarens position eller preferenser. Det är utmanande att utveckla den teknik som krävs för att kunna sätta in annonser dynamiskt i strömmade videofiler. Till exempel är det viktigt att tänka på följande: synkronisering av strömmad innehåll, val av lämplig transportformat för videoleveransen och gränsen för skarvning (så kallad splicing boundary). Detaljerna kring denna teknik finns i denna avhandling. Vi har forskat på att hitta det bästa transportformatet för videoleverans och vi har studerat relevant arbete som gjorts tidigare för att hitta en lämplig mekanism för dynamisk annonsinsättning. Baserat på vår forskning och studerande av tidigare arbeten har vi klassificerat det bästa formatet för leveransen av videostycken, implementerat och evaluerat en teknik för annonsinlägg.

Diploma work „Multimedia player for iOS“ deals with the description of the video formats such as HLS, MP4, MPEG Transport Stream, and Dash. Next, it continues with protected content DRM, VAST advertisements and analytics tool called Google Analytics. During compilation of this diploma work was created a framework in programming language Swift. This framework is basically a player, that besides playing Interactive videos is able to show ads, subtitles, can change the video quality, AirPlay and download the content for offline playing. A feature to play a protected content could not be done as a developer's request for a product deployment SDK Fairplay was refused from an SDK owner. A testing app was also created that implements developed a framework and prove the functionality of the developed functions.

This thesis aims to create an advanced multimedia player with support of an adaptive stream, securing of the multimedia content and support for playing advertisement. The first part explains the used technologies followed by an analysis of available solutions and the comparison between the two. The theoretical framework is then used to create a player library and a testing application. The player library is based on the framework of ExoPlayer application. Whole application is created in Java for the operation system Android OS.

<p dir="ltr">The rapid development of machine learning techniques makes it possible to manipulate or synthesize video and audio information while introducing nearly indetectable artifacts. Most media forensics methods analyze the high-level data (e.g., pixels from videos, temporal signals from audios) decoded from compressed media data. Since media manipulation or synthesis methods usually aim to improve the quality of such high-level data directly, acquiring forensic evidence from these data has become increasingly challenging. In this work, we focus on media forensics techniques using the metadata in media formats, which includes container metadata and coding parameters in the encoded bitstream. Since many media manipulation and synthesis methods do not attempt to hide metadata traces, it is possible to use them for forensics tasks. First, we present a video forensics technique using metadata embedded in MP4/MOV video containers. Our proposed method achieved high performance in video manipulation detection, source device attribution, social media attribution, and manipulation tool identification on publicly available datasets. Second, we present a transformer neural network based MP3 audio forensics technique using low-level codec information. Our proposed method can localize multiple compressed segments in MP3 files. The localization accuracy of our proposed method is higher compared to other methods. Third, we present an H.264-based video device matching method. This method can determine if the two video sequences are captured by the same device even if the method has never encountered the device. Our proposed method achieved good performance in a three-fold cross validation scheme on a publicly available video forensics dataset containing 35 devices. Fourth, we present a Graph Neural Network (GNN) based approach for the analysis of MP4/MOV metadata trees. The proposed method is trained using Self-Supervised Learning (SSL), which increased the robustness of the proposed method and makes it capable of handling missing/unseen data. Fifth, we present an efficient approach to compute the spectrogram feature with MP3 compressed audio signals. The proposed approach decreases the complexity of speech feature computation by ~77.6% and saves ~37.87% of MP3 decoding time. The resulting spectrogram features lead to higher synthetic speech detection performance.</p>