Twofold Video Hashing with Automatic Synchronization

Video hashing finds a wide array of applications in content authentication, robust retrieval and anti-piracy search. While much of the existing research has focused on extracting robust and secure content descriptors, a significant open challenge still remains: Most existing video hashing methods are fallible to temporal desynchronization. That is, when the query video results by deleting or inserting some frames from the reference video, most existing methods assume the positions of the deleted (or inserted) frames are either perfectly known or reliably estimated. This assumption may be okay under typical transcoding and frame-rate changes but is highly inappropriate in adversarial scenarios such as anti-piracy video search. For example, an illegal uploader will try to bypass the 'piracy check' mechanism of YouTube/Dailymotion etc by performing a cleverly designed non-uniform resampling of the video. We present a new solution based on dynamic time warping (DTW), which can implement automatic synchronization and can be used together with existing video hashing methods. The second contribution of this paper is to propose a new robust feature extraction method called flow hashing (FH), based on frame averaging and optical flow descriptors. Finally, a fusion mechanism called distance boosting is proposed to combine the information extracted by DTW and FH. Experiments on real video collections show that such a hash extraction and comparison enables unprecedented robustness under both spatial and temporal attacks.Comment: submitted to Image Processing (ICIP), 2014 21st IEEE International Conference o

Automatic Synchronization of Multi-User Photo Galleries

In this paper we address the issue of photo galleries synchronization, where pictures related to the same event are collected by different users. Existing solutions to address the problem are usually based on unrealistic assumptions, like time consistency across photo galleries, and often heavily rely on heuristics, limiting therefore the applicability to real-world scenarios. We propose a solution that achieves better generalization performance for the synchronization task compared to the available literature. The method is characterized by three stages: at first, deep convolutional neural network features are used to assess the visual similarity among the photos; then, pairs of similar photos are detected across different galleries and used to construct a graph; eventually, a probabilistic graphical model is used to estimate the temporal offset of each pair of galleries, by traversing the minimum spanning tree extracted from this graph. The experimental evaluation is conducted on four publicly available datasets covering different types of events, demonstrating the strength of our proposed method. A thorough discussion of the obtained results is provided for a critical assessment of the quality in synchronization.Comment: ACCEPTED to IEEE Transactions on Multimedi

Perceptual Video Hashing for Content Identification and Authentication

Perceptual hashing has been broadly used in the literature to identify similar contents for video copy detection. It has also been adopted to detect malicious manipulations for video authentication. However, targeting both applications with a single system using the same hash would be highly desirable as this saves the storage space and reduces the computational complexity. This paper proposes a perceptual video hashing system for content identification and authentication. The objective is to design a hash extraction technique that can withstand signal processing operations on one hand and detect malicious attacks on the other hand. The proposed system relies on a new signal calibration technique for extracting the hash using the discrete cosine transform (DCT) and the discrete sine transform (DST). This consists of determining the number of samples, called the normalizing shift, that is required for shifting a digital signal so that the shifted version matches a certain pattern according to DCT/DST coefficients. The rationale for the calibration idea is that the normalizing shift resists signal processing operations while it exhibits sensitivity to local tampering (i.e., replacing a small portion of the signal with a different one). While the same hash serves both applications, two different similarity measures have been proposed for video identification and authentication, respectively. Through intensive experiments with various types of video distortions and manipulations, the proposed system has been shown to outperform related state-of-the art video hashing techniques in terms of identification and authentication with the advantageous ability to locate tampered regions

Robust Image Hashing Based Efficient Authentication for Smart Industrial Environment

[EN] Due to large volume and high variability of editing tools, protecting multimedia contents, and ensuring their privacy and authenticity has become an increasingly important issue in cyber-physical security of industrial environments, especially industrial surveillance. The approaches authenticating images using their principle content emerge as popular authentication techniques in industrial video surveillance applications. But maintaining a good tradeoff between perceptual robustness and discriminations is the key research challenge in image hashing approaches. In this paper, a robust image hashing method is proposed for efficient authentication of keyframes extracted from surveillance video data. A novel feature extraction strategy is employed in the proposed image hashing approach for authentication by extracting two important features: the positions of rich and nonzero low edge blocks and the dominant discrete cosine transform (DCT) coefficients of the corresponding rich edge blocks, keeping the computational cost at minimum. Extensive experiments conducted from different perspectives suggest that the proposed approach provides a trustworthy and secure way of multimedia data transmission over surveillance networks. Further, the results vindicate the suitability of our proposal for real-time authentication and embedded security in smart industrial applications compared to state-of-the-art methods.This work was supported in part by the National Natural Science Foundation of China under Grant 61976120, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20191445, in part by the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-048, and sponsored by Qing Lan Project of Jiangsu Province, China.Sajjad, M.; Ul Haq, I.; Lloret, J.; Ding, W.; Muhammad, K. (2019). Robust Image Hashing Based Efficient Authentication for Smart Industrial Environment. IEEE Transactions on Industrial Informatics. 15(12):6541-6550. https://doi.org/10.1109/TII.2019.2921652S65416550151

SISTEMI PER LA MOBILITÀ DEGLI UTENTI E DEGLI APPLICATIVI IN RETI WIRED E WIRELESS

The words mobility and network are found together in many contexts. The issue alone of modeling geographical user mobility in wireless networks has countless applications. Depending on one’s background, the concept is investigated with very different tools and aims. Moreover, the last decade saw also a growing interest in code mobility, i.e. the possibility for soft-ware applications (or parts thereof) to migrate and keeps working in different devices and environ-ments. A notable real-life and successful application is distributed computing, which under certain hypothesis can void the need of expensive supercomputers. The general rationale is splitting a very demanding computing task into a large number of independent sub-problems, each addressable by limited-power machines, weakly connected (typically through the Internet, the quintessence of a wired network). Following this lines of thought, we organized this thesis in two distinct and independent parts: Part I It deals with audio fingerprinting, and a special emphasis is put on the application of broadcast mon-itoring and on the implementation aspects. Although the problem is tackled from many sides, one of the most prominent difficulties is the high computing power required for the task. We thus devised and operated a distributed-computing solution, which is described in detail. Tests were conducted on the computing cluster available at the Department of Engineering of the University of Ferrara. Part II It focuses instead on wireless networks. Even if the approach is quite general, the stress is on WiFi networks. More specifically, we tried to evaluate how mobile-users’ experience can be improved. Two tools are considered. In the first place, we wrote a packet-level simulator and used it to esti-mate the impact of pricing strategies in allocating the bandwidth resource, finding out the need for such solutions. Secondly, we developed a high-level simulator that strongly advises to deepen the topic of user cooperation for the selection of the “best” point of access, when many are available. We also propose one such policy

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Application of Digital Fingerprinting: Duplicate Image Detection

Identifying the content automatically is the most necessary condition to detect and fight piracy. Watermarking the image is the most basic and common technique to fight piracy. But the effectiveness of watermark is limited. Image fingerprinting provides an alternate and efficient solution for managing and identifying the multimedia content. After registering the original image contents, by comparing the colluded image with the original one, the percentage of distortion can be calculated. In this paper presented are one such fingerprinting-based forensic application: Duplicate image detection. To authenticate image content perceptual hash is an efficient solution. Perceptual hashes of almost similar images or near duplicate images are very similar to each other making it easier to compare images unlike cryptographic hashes which vary very radically even in the case of small distortions. Potential applications are unlimited including digital forensics, protection of copyrighted material etc. However, conventional image hash algorithms only offer a limited authentication level for the protection of overall content. In this work, we compared and contrasted different perceptual hashes and proposed a image hashing algorithm which is an excellent trade off of accuracy and speed

Feature-based calibration of distributed smart stereo camera networks

A distributed smart camera network is a collective of vision-capable devices with enough processing power to execute algorithms for collaborative vision tasks. A true 3D sensing network applies to a broad range of applications, and local stereo vision capabilities at each node offer the potential for a particularly robust implementation. A novel spatial calibration method for such a network is presented, which obtains pose estimates suitable for collaborative 3D vision in a distributed fashion using two stages of registration on robust 3D features. The method is first described in a general, modular sense, assuming some ideal vision and registration algorithms. Then, existing algorithms are selected for a practical implementation. The method is designed independently of networking details, making only a few basic assumptions about the underlying network\u27s capabilities. Experiments using both software simulations and physical devices are designed and executed to demonstrate performance