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With the fast increase of multimedia contents, efficient forensics investigation methods for multimedia files have been required. In multimedia files, the similarity means that the identical media (audio and video) data are existing among multimedia files. This paper proposes an efficient multimedia file forensics system based on file similarity search of video contents. The proposed system needs two key techniques. First is a media-aware information detection technique. The first critical step for the similarity search is to find the meaningful keyframes or key sequences in the shots through a multimedia file, in order to recognize altered files from the same source file. Second is a video fingerprint-based technique (VFB) for file similarity search. The byte for byte comparison is an inefficient similarity searching method for large files such as multimedia. The VFB technique is an efficient method to extract video features from the large multimedia files. It also provides an independent media-aware identification method for detecting alterations to the source video file (e.g., frame rates, resolutions, and formats, etc.). In this paper, we focus on two key challenges: to generate robust video fingerprints by finding meaningful boundaries of a multimedia file, and to measure video similarity by using fingerprint-based matching. Our evaluation shows that the proposed system is possible to apply to realistic multimedia file forensics tools.

In digital pathology, image similarity algorithms are used to find cancer in tissue cells from medical images. However, it is very difficult to apply image similarity algorithms used in general purpose system. Because in the medical field, accuracy and reliability must be perfect when looking for cancer cells by using image similarity techniques to pathology images. To cope with this problem, this paper proposes an efficient similar image search algorithm for digital pathology by applying leveling and tiling scheme on OpenSlide format. Furthermore, we apply image sync method to extract feature key points during image similarity processing. In the experiment, to prove the efficiency of the proposed system, we conduct several experiments including algorithm performance, algorithm accuracy and computation time. The experiments result shows that the proposed system efficiently retrieves similar cell images from pathology images.

Augmented Reality (AR)–based video telephony service can allow mobile users a better user experience (UX) since it allows participants to place and transmit augmented objects on video frames to a peer. However, there are quite a few AR-based mobile video communication models today, yet the existing models are limited and insufficient in supporting technical service such as real-time object detection, dynamic data selection, and discrimination between local data augmentation and remote data augmentation. This paper presents an enhanced AR–based mobile video telephony scheme, in which the object of interest can be dynamically combined with a video frame through real-time object detection, and users can immediately share their experience with their friend during a video call. In order to evaluate the effectiveness and feasibility of the proposed scheme, an application has been implemented on the mobile system and the computational time has been measured. Experimental results show that the proposed system can give customers better UX with small increase of computational time.

File similarity is a numerical indicator that how many duplicated data exist in target files. With this information, we can reduce storage capacity with data deduplication scheme, further it can be exploited in the digital forensic field for finding malicious software. However, measuring file similarity between files can cause a high overhead in terms of processing time and the capacity of disk storage. For this reason, in this paper, we propose a novel file similarity evaluation algorithm called PHISA (Partial Hash Information String Algorithm). To evaluate the performance of the proposed system, we compare PHISA to well-known file similarity tools. The evaluation result shows that PHISA reduces the processing time and increases the similarity evaluation accuracy.

Image display time is defined as the time taken to load, decode, and render images on a screen. When users attempt to open images stored on a mobile phone, they can sometimes feel that it takes a lot of time to display the images. This paper analyzes the various causes of the delay and presents ideas to reduce the time and improve the customer’s experience. To evaluate these ideas, an enhanced image viewer application was implemented and evaluated quantitatively and qualitatively by comparing it to the existing viewer. Experimental results show that the proposed solutions can reduce image display time by more than 10 % for high-pixel images. In addition, it can reduce thumbnail construction times effectively.

So far, the data anonymization approaches based on k-anonymity and l-diversity has contributed much to privacy protection from record and attributes linkage attacks. However, the existing solutions are not efficient when applied to multimedia Big Data anonymization. This paper analyzes this problem in detail in terms of the processing time, memory space, and usability, and presents two schemes to overcome such inefficiency. The first one is to reduce the processing time and space by minimizing the temporary buffer usage during anonymization process. The second is to construct an early taxonomy during the database design. The idea behind this approach is that database designers should take preliminary actions for anonymization during the early stage of a database design to alleviate the burden placed on data publishers. To evaluate the effectiveness and feasibility of these schemes, specific application tools based on the proposed approaches were implemented and experiments were conducted.