Anurag Tripathi

Bio: Anurag Tripathi is an academic researcher from Indian Institute of Technology Delhi. The author has contributed to research in topics: Optical switch & Deep learning. The author has an hindex of 2, co-authored 12 publications receiving 20 citations. Previous affiliations of Anurag Tripathi include Indian Institute of Technology, Jodhpur & Samsung.

CNN-based Multiple Manipulation Detector Using Frequency Domain Features of Image Residuals

Abstract: Increasingly sophisticated image editing tools make it easy to modify images. Often these modifications are elaborate, convincing, and undetectable by even careful human inspection. These considerations have prompted the development of forensic algorithms and approaches to detect modifications done to an image. However, these detectors are model-driven (i.e., manipulation-specific) and the choice of a potent detector requires knowledge of the type of manipulation, something that cannot be known (a priori). Thus, the latest effort is directed towards developing model-free (i.e., generalized) detectors capable of detecting multiple manipulation types. In this article, we propose a novel detector capable of exposing seven different manipulation types in low-resolution compressed images. Our proposed approach is based on a two-layer convolutional neural network (CNN) to extract frequency domain features of image median filtered residual that are classified using two different classifiers—softmax and extremely randomized trees. Extensive experiments demonstrate the efficacy of proposed detector over existing state-of-the-art detectors.

Ontology based context aware situation tracking

Abstract: Ubiquitous intelligent devices have enabled provision of smart services to people in seamless way. Context-awareness helps understand current state-of-affairs or the situation in which presently the system is. This understanding helps the IoT application provide more relevant and smarter services based on situations that change over a period of time. In this paper, we propose a novel context-aware situation-tracking framework that makes use of an ontology. The ontology represents the conceptual model of a dynamic world, where situations evolve over time in changing contexts. The ontology provides the reasoning framework to infer about a situation based on the input context data as well as the past information of earlier situations. Future situations can be predicted with some belief based on current situation and incoming context data. The context data is acquired from sensor devices and external inputs. For every recognized situation, system recommends some actions to provide context-aware service. We use Multimedia Web Ontology Language (MOWL) to represents the ontology. MOWL proposes a probabilistic framework for reasoning with uncertainties linked with observation of context. It makes use of Dynamic Bayesian networks to predict and track the dynamically changing situations. We illustrate use of this framework for Smart Mirror use case.

Image super resolution using distributed locality sensitive hashing for manifold learning

Abstract: In this paper we propose a distributed locality sensitive hashing based framework for image super resolution exploiting computational and storage efficiency of cloud. Now days huge multimedia data is available on the cloud which can be utilized using store anywhere and excess anywhere model. It may be noted that super resolution is required for consumer electronics display devices due to various reasons. The propose framework exploits the image correlation for image super resolution using locality sensitive hashing (LSH) for manifold learning. In our work we have exploited the benefits of manifold learning for image super resolution, which in-turn is a highly time complex operation. The time complexity is involved due to finding the approximate nearest neighbors from trillion of image patches for locally linear embedding (LLE) operation. In our approach it is mitigated by using a distributed framework which internally uses hash tables for mapping of patches in the target image from a database of internet picture collection. The proposed framework for super resolution provides promising results in comparison to existing approaches.

Protecting composite IoT server by secure secret key exchange for XEN intra virtual machines

Abstract: Challenges presented by ever increasing volumes of smart objects are huge in terms of security and privacy of data generated out of these objects. These challenges range from the usage scenario with other objects and dedicated servers which act as a middleman among various networks such as internet and wireless sensor network. In this paper, attempt has been made to highlight a simple use case of a composite server in a XEN-based virtualised system. Security threats like sniffing and spoofing are isolated and analysed with respect to a novel key transfer method between host and guest operating system in XEN-based virtualised system. In addition, secure and trust model to cater the said security threats like sniffing and spoofing is also presented. The performance of the proposed system in terms of CPU usage and network bandwidth is also shown.

A hybrid approach for image super-resolution of low depth of field images

Abstract: Multimedia content plays significant role in our day to day life. Nowadays devices like mobile, tablets, Digital TV have capability to display high resolution images for better viewing experience. Therefore the need arises to enhance the resolution of available images in real time for better perceptual quality. This paper focuses on super-resolution of shallow depth of field images which are widely used in macro, portrait or sports photography. The proposed hybrid approach first segments the low resolution image into the object of interest (foreground) and background. Subsequently, super resolve the object of interest by sparse representation and background by traditional interpolation approaches. This approach helps to reduce the overall time complexity and enhance the quality by extracting significant features from object of interest and reduce computations for shallow depth of field images. Experimentally it has been inferred that hybrid approach performs better than existing state-of-the-art super-resolution approaches in terms of time complexity.

Secure Service Offloading for Internet of Vehicles in SDN-Enabled Mobile Edge Computing

Abstract: Currently, Edge computing (EC) paradigm is adopted to provision the low-latency resources for the massive real-time services in Internet of vehicles (IoV). To alleviate the QoE (Quality of Experience) degradation of the vehicular users due to the uncertainties (e.g., resource conflicts and communicating interruption), software-defined network (SDN) is involved in the EC-enabled IoV to manage the cooperative operation of distributed edge nodes (ENs). However, the increasing privacy leakage for the IoV service offloading causes the disclosure of the sensitive information, including driving location, personal information of the driver, etc. Moreover, the regulation of SDN is practically insufficient, as the general control is incompetent to maintain balanced operation with the premise of efficient service utility. In view of these challenges, a secure s ervice o ffloading me thod, named SOME, is designed to promote IoV service utility and edge utility, meanwhile ensuring privacy security, in SDN-enabled EC. Specifically, an SDN-based framework for IoV service management is developed to address the inherent uncertainty of edge network by SDN controllers. Besides, the locality-sensitive-hash (LSH) is leveraged to realize utility- and privacy-aware service selection. Eventually, comparative experiments are implemented to verify the effectiveness of SOME.

An IoT approach for context-aware smart traffic management using ontology

Abstract: This paper exhibits a novel context-aware service framework for IoT based Smart Traffic Management using ontology to regulate smooth traffic flow in smart cities by analyzing real-time traffic environment. The proposed approach makes smarter use of transport networks to achieve objectives related to performance of transport system. This requires efficient traffic planning measures which relate to the actions designed to adjust the demand and capacity of the network in time and space by use of IoT technologies. The adoption of sensors and IoT devices in Smart Traffic System helps to capture the user's preferences and context information which can be in the form of travel time, weather conditions or real-life driving patterns. We have employed multimedia ontology to derive higher level descriptions of traffic conditions and vehicles from perceptual observation of traffic information which provides important grounds for our proposed IoT framework. The multimedia ontology encoded in Multimedia Web Ontology Language(MOWL) helps to define classes, properties, and structure of a possible traffic environment to provide insights across the transportation network. MOWL supports Dynamic Bayesian networks (DBN) to deal with time-series data and uncertainties linked with context observations which fits the definition of an intelligent IoT system. Thus, our proposed smart traffic framework aggregates information corresponding to traffic domain such as traffic videos captured using CCTV cameras and allows automatic prediction of dynamically changing situations which helps to make traffic authorities more responsive. We have illustrated use of our approach by utilizing contextual information, to assess real-time congestion situation on roads thus allowing to visualize planning services. Once the congestion situation is predicted, alternate congestion free routes which are in accordance with the coveted criteria are suggested that can be propagated through text-messages or e-mails to the users.