Intelligent video editing techniques can be used to tamper videos such as surveillance camera videos, defeating their potential to be used as evidence in a court of law. In this paper, we propose a technique to detect forgery in MPEG videos by analyzing the frame's compression noise characteristics. The compression noise is extracted from spatial domain by using a modified Huber Markov Random Field (HMRF) as a prior for image. The transition probability matrices of the extracted noise are used as features to classify a given video as single compressed or double compressed. The experiment is conducted on different YUV sequences with different scale factors. The efficiency of our classification is observed to be higher relative to the state of the art detection algorithms.
We propose a 'Touch-less finger print system', the hardware of which is limited to just a webcam and a processor. The present biometric systems are either costly or unhygienic, as a person who is perfectly healthy may acquire a disease from the system which was already used by a diseased person. For various reasons, users are concerned about touching the biometric scanners. Even then fingerprint remains to be the most unique feature of any person. Webcams are concomitant with today's PCs and laptops making it easier to implement our system. Our proposal is basically a software that can be implemented anywhere with access to a PC and a webcam. We use a web camera to capture the user's finger at a distance for recognition. The finger image so obtained is isolated from the background and ridges are extracted. The finger print is then processed in Euclidean space to obtain the minutiae points. Spurious minutiae are removed and the orientations and Euclidean distances of the minutiae points are saved for future matching. The main applications of this system widens to almost all fields especially Personal security (PC, Laptops and Mobile Phones), Defense, Attendance, ATM etc.
Software development is becoming increasingly open and collaborative with the advent of platforms such as GitHub. Given its crucial role, there is a need to better understand and model the dynamics of GitHub as a social platform. Previous work has mostly considered the dynamics of traditional social networking sites like Twitter and Facebook. We propose GitEvolve, a system to predict the evolution of GitHub repositories and the different ways by which users interact with them. To this end, we develop an end-to-end multi-task sequential deep neural network that given some seed events, simultaneously predicts which user-group is next going to interact with a given repository, what the type of the interaction is, and when it happens. To facilitate learning, we use graph based representation learning to encode relationship between repositories. We map users to groups by modelling common interests to better predict popularity and to generalize to unseen users during inference. We introduce an artificial event type to better model varying levels of activity of repositories in the dataset. The proposed multi-task architecture is generic and can be extended to model information diffusion in other social networks. In a series of experiments, we demonstrate the effectiveness of the proposed model, using multiple metrics and baselines. Qualitative analysis of the model's ability to predict popularity and forecast trends proves its applicability 1 .
CCS CONCEPTS• Information systems → Social networks; • Computing methodologies → Neural networks; • Human-centered computing → Social networks.
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