Sevinc Bayram (Polytechnic Institute of New York University), “Applications of multimedia forensics”, Advisor: Prof. Nasir Memon (2012) In recent years, the problem of multimedia source verification has received rapidly growing attention. To determine the source of a multimedia object (image/video) several techniques have been developed that can identify characteristics that relate to the physical processes and algorithms used in their generation. In particular, it has been shown that `noise-like' variations in images and videos, due to the different light sensitivity of pixels, can be accurately measured, and used as a fingerprint of an imaging sensor. The presence of a sensor fingerprint in a multimedia object would provide evidence that the given multimedia object was captured by that exact sensor. Motivated by this, in this thesis, the author investigates the different application potentials of the sensor fingerprint matching technique. The author first focuses on using sensor fingerprints in a source identification scenario where the aim is to find the multimedia objects captured by a given device in a large collection of multimedia objects, in a timely fashion. The associated fingerprint matching method itself can be computationally expensive. To overcome the limitations, we propose two different approaches. In the first approach, the author proposes to represent sensor fingerprints in binary-quantized form. Experiments on actual sensor fingerprint data are conducted to confirm that there's only a slight increase in the probability of error and to demonstrate the computational efficacy of the approach. In the second approach, the author presents a binary search tree (BST) data structure based on group testing to enable the fast identification. The results on the real-world and simulation data show that with the proposed scheme major improvement in search time can be achieved. The limitations of the BST are also shown analytically. Furthermore, the author demonstrates how to use device characteristics for conventional content-based video copy detection task. For details, please view here or contact the author.