Nowadays, powerful and easy to use editing software which is available to almost everyone allows forgers to create convincing digital forgeries. As multimedia applications require a certain level of trust in the integrity and authenticity of the data become more common, there is an increasing need to restore some of the lost trustworthiness of digital media. In multimedia forensics, Digital Signature and Digital Watermarking have long been commonly used in video authentication, but these methods have proven to have shortcomings. The main drawback of these techniques is that information must generally be inserted at the time of video capture or before video broadcasting. Both techniques require two stages are: at the sender side and then at the receiver side, which in some real world applications is not feasible. For the problem of source type identification, digital fingerprints are usually extracted and then compared with a dataset of possible fingerprints to determine the acquisition devices. Photo-Response Non-Uniformity (PRNU), which is caused by the different sensitivity of pixels to the light, has proven to be a distinctive link between the camera and its images/videos. With this in mind, this thesis proposes several new digital forensic techniques to detect evidence of manipulations in digital video content based on blind techniques (Chapter 4 and Chapter 5) where there is no need for per-embedded watermarks or per-generated digital signature. These methods showed potential to be reliable techniques in digital video authentication based on the local video information. For the problem of determining the source of digital evidence, this thesis proposes a G-PRNU method (in Chapter 3) that overcomes the accuracy obtained in PRNU method in the problem of digital videos source type identification and it is less computationally expensive. Each proposed method was tested on a dataset of videos and detailed experimental results are presented.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:725326 |
| Date | January 2017 |
| Creators | Al-Athamneh, Mohammad Hmoud |
| Publisher | Queen's University Belfast |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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