Digital rights management techniques for H.264 video

This work aims to present a number of low-complexity digital rights management (DRM) methodologies for the H.264 standard. Initially, requirements to enforce DRM are analyzed and understood. Based on these requirements, a framework is constructed which puts forth different possibilities that can be explored to satisfy the objective. To implement computationally efficient DRM methods, watermarking and content based copy detection are then chosen as the preferred methodologies. The first approach is based on robust watermarking which modifies the DC residuals of 4×4 macroblocks within I-frames. Robust watermarks are appropriate for content protection and proving ownership. Experimental results show that the technique exhibits encouraging rate-distortion (R-D) characteristics while at the same time being computationally efficient. The problem of content authentication is addressed with the help of two methodologies: irreversible and reversible watermarks. The first approach utilizes the highest frequency coefficient within 4×4 blocks of the I-frames after CAVLC en- tropy encoding to embed a watermark. The technique was found to be very effect- ive in detecting tampering. The second approach applies the difference expansion (DE) method on IPCM macroblocks within P-frames to embed a high-capacity reversible watermark. Experiments prove the technique to be not only fragile and reversible but also exhibiting minimal variation in its R-D characteristics. The final methodology adopted to enforce DRM for H.264 video is based on the concept of signature generation and matching. Specific types of macroblocks within each predefined region of an I-, B- and P-frame are counted at regular intervals in a video clip and an ordinal matrix is constructed based on their count. The matrix is considered to be the signature of that video clip and is matched with longer video sequences to detect copies within them. Simulation results show that the matching methodology is capable of not only detecting copies but also its location within a longer video sequence. Performance analysis depict acceptable false positive and false negative rates and encouraging receiver operating charac- teristics. Finally, the time taken to match and locate copies is significantly low which makes it ideal for use in broadcast and streaming applications

Content-based video copy detection using multimodal analysis

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 67-76.Huge and increasing amount of videos broadcast through networks has raised the need of automatic video copy detection for copyright protection. Recent developments in multimedia technology introduced content-based copy detection (CBCD) as a new research field alternative to the watermarking approach for identification of video sequences. This thesis presents a multimodal framework for matching video sequences using a three-step approach: First, a high-level face detector identifies facial frames/shots in a video clip. Matching faces with extended body regions gives the flexibility to discriminate the same person (e.g., an anchor man or a political leader) in different events or scenes. In the second step, a spatiotemporal sequence matching technique is employed to match video clips/segments that are similar in terms of activity. Finally the non-facial shots are matched using low-level visual features. In addition, we utilize fuzzy logic approach for extracting color histogram to detect shot boundaries of heavily manipulated video clips. Methods for detecting noise, frame-droppings, picture-in-picture transformation windows, and extracting mask for still regions are also proposed and evaluated. The proposed method was tested on the query and reference dataset of CBCD task of TRECVID 2008. Our results were compared with the results of top-8 most successful techniques submitted to this task. Experimental results show that the proposed method performs better than most of the state-of-the-art techniques, in terms of both effectiveness and efficiency.Küçüktunç, OnurM.S

Fine-grained Incident Video Retrieval with Video Similarity Learning.

PhD ThesesIn this thesis, we address the problem of Fine-grained Incident Video Retrieval (FIVR) using video similarity learning methods. FIVR is a video retrieval task that aims to retrieve all videos that depict the same incident given a query video { related video retrieval tasks adopt either very narrow or very broad scopes, considering only nearduplicate or same event videos. To formulate the case of same incident videos, we de ne three video associations taking into account the spatio-temporal spans captured by video pairs. To cover the benchmarking needs of FIVR, we construct a large-scale dataset, called FIVR-200K, consisting of 225,960 YouTube videos from major news events crawled from Wikipedia. The dataset contains four annotation labels according to FIVR de nitions; hence, it can simulate several retrieval scenarios with the same video corpus. To address FIVR, we propose two video-level approaches leveraging features extracted from intermediate layers of Convolutional Neural Networks (CNN). The rst is an unsupervised method that relies on a modi ed Bag-of-Word scheme, which generates video representations from the aggregation of the frame descriptors based on learned visual codebooks. The second is a supervised method based on Deep Metric Learning, which learns an embedding function that maps videos in a feature space where relevant video pairs are closer than the irrelevant ones. However, videolevel approaches generate global video representations, losing all spatial and temporal relations between compared videos. Therefore, we propose a video similarity learning approach that captures ne-grained relations between videos for accurate similarity calculation. We train a CNN architecture to compute video-to-video similarity from re ned frame-to-frame similarity matrices derived from a pairwise region-level similarity function. The proposed approaches have been extensively evaluated on FIVR- 200K and other large-scale datasets, demonstrating their superiority over other video retrieval methods and highlighting the challenging aspect of the FIVR problem

Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints

The continuous stream of videos that are uploaded and shared on the Internet has been leveraged by computer vision researchers for a myriad of detection and retrieval tasks, including gesture detection, copy detection, face authentication, etc. However, the existing state-of-the-art event detection and retrieval techniques fail to deal with several real-world challenges (e.g., low resolution, low brightness and noise) under adversary constraints. This dissertation focuses on these challenges in realistic scenarios and demonstrates practical methods to address the problem of robustness and efficiency within video event detection and retrieval systems in five application settings (namely, CAPTCHA decoding, face liveness detection, reconstructing typed input on mobile devices, video confirmation attack, and content-based copy detection). Specifically, for CAPTCHA decoding, I propose an automated approach which can decode moving-image object recognition (MIOR) CAPTCHAs faster than humans. I showed that not only are there inherent weaknesses in current MIOR CAPTCHA designs, but that several obvious countermeasures (e.g., extending the length of the codeword) are not viable. More importantly, my work highlights the fact that the choice of underlying hard problem selected by the designers of a leading commercial solution falls into a solvable subclass of computer vision problems. For face liveness detection, I introduce a novel approach to bypass modern face authentication systems. More specifically, by leveraging a handful of pictures of the target user taken from social media, I show how to create realistic, textured, 3D facial models that undermine the security of widely used face authentication solutions. My framework makes use of virtual reality (VR) systems, incorporating along the way the ability to perform animations (e.g., raising an eyebrow or smiling) of the facial model, in order to trick liveness detectors into believing that the 3D model is a real human face. I demonstrate that such VR-based spoofing attacks constitute a fundamentally new class of attacks that point to a serious weaknesses in camera-based authentication systems. For reconstructing typed input on mobile devices, I proposed a method that successfully transcribes the text typed on a keyboard by exploiting video of the user typing, even from significant distances and from repeated reflections. This feat allows us to reconstruct typed input from the image of a mobile phone’s screen on a user’s eyeball as reflected through a nearby mirror, extending the privacy threat to include situations where the adversary is located around a corner from the user. To assess the viability of a video confirmation attack, I explored a technique that exploits the emanations of changes in light to reveal the programs being watched. I leverage the key insight that the observable emanations of a display (e.g., a TV or monitor) during presentation of the viewing content induces a distinctive flicker pattern that can be exploited by an adversary. My proposed approach works successfully in a number of practical scenarios, including (but not limited to) observations of light effusions through the windows, on the back wall, or off the victim’s face. My empirical results show that I can successfully confirm hypotheses while capturing short recordings (typically less than 4 minutes long) of the changes in brightness from the victim’s display from a distance of 70 meters. Lastly, for content-based copy detection, I take advantage of a new temporal feature to index a reference library in a manner that is robust to the popular spatial and temporal transformations in pirated videos. My technique narrows the detection gap in the important area of temporal transformations applied by would-be pirates. My large-scale evaluation on real-world data shows that I can successfully detect infringing content from movies and sports clips with 90.0% precision at a 71.1% recall rate, and can achieve that accuracy at an average time expense of merely 5.3 seconds, outperforming the state of the art by an order of magnitude.Doctor of Philosoph

FIVR: Fine-Grained Incident Video Retrieval

This paper introduces the problem of Fine-grained Incident Video Retrieval (FIVR). Given a query video, the objective is to retrieve all associated videos, considering several types of associations that range from duplicate videos to videos from the same incident. FIVR offers a single framework that contains several retrieval tasks as special cases. To address the benchmarking needs of all such tasks, we construct and present a large-scale annotated video dataset, which we call FIVR-200K, and it comprises 225,960 videos. To create the dataset, we devise a process for the collection of YouTube videos based on major news events from recent years crawled from Wikipedia and deploy a retrieval pipeline for the automatic selection of query videos based on their estimated suitability as benchmarks. We also devise a protocol for the annotation of the dataset with respect to the four types of video associations defined by FIVR. Finally, we report the results of an experimental study on the dataset comparing five state-of-the-art methods developed based on a variety of visual descriptors, highlighting the challenges of the current problem

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Hough transform generated strong image hashing scheme for copy detection

The rapid development of image editing software has resulted in widespread unauthorized duplication of original images. This has given rise to the need to develop robust image hashing technique which can easily identify duplicate copies of the original images apart from differentiating it from different images. In this paper, we have proposed an image hashing technique based on discrete wavelet transform and Hough transform, which is robust to large number of image processing attacks including shifting and shearing. The input image is initially pre-processed to remove any kind of minor effects. Discrete wavelet transform is then applied to the pre-processed image to produce different wavelet coefficients from which different edges are detected by using a canny edge detector. Hough transform is finally applied to the edge-detected image to generate an image hash which is used for image identification. Different experiments were conducted to show that the proposed hashing technique has better robustness and discrimination performance as compared to the state-of-the-art techniques. Normalized average mean value difference is also calculated to show the performance of the proposed technique towards various image processing attacks. The proposed copy detection scheme can perform copy detection over large databases and can be considered to be a prototype for developing online real-time copy detection system

A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges

Measuring and evaluating source code similarity is a fundamental software engineering activity that embraces a broad range of applications, including but not limited to code recommendation, duplicate code, plagiarism, malware, and smell detection. This paper proposes a systematic literature review and meta-analysis on code similarity measurement and evaluation techniques to shed light on the existing approaches and their characteristics in different applications. We initially found over 10000 articles by querying four digital libraries and ended up with 136 primary studies in the field. The studies were classified according to their methodology, programming languages, datasets, tools, and applications. A deep investigation reveals 80 software tools, working with eight different techniques on five application domains. Nearly 49% of the tools work on Java programs and 37% support C and C++, while there is no support for many programming languages. A noteworthy point was the existence of 12 datasets related to source code similarity measurement and duplicate codes, of which only eight datasets were publicly accessible. The lack of reliable datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm languages are the main challenges in the field. Emerging applications of code similarity measurement concentrate on the development phase in addition to the maintenance.Comment: 49 pages, 10 figures, 6 table

Information exchange between medical databases through automated identification of concept equivalence

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2002."February 2002."Includes bibliographical references (p. 123-127).The difficulty of exchanging information between heterogeneous medical databases remains one of the chief obstacles in achieving a unified patient medical record. Although methods have been developed to address differences in data formats, system software, and communication protocols, automated data exchange between disparate systems still remains an elusive goal. The Medical Information Acquisition and Transmission Enabler (MEDIATE) system identifies semantically equivalent concepts between databases to facilitate information exchange. MEDIATE employs a semantic network representation to model underlying native databases and to serve as an interface for database queries. This representation generates a semantic context for data concepts that can subsequently be exploited to perform automated concept matching between disparate databases. To test the feasibility of this system, medical laboratory databases from two different institutions were represented within MEDIATE and automated concept matching was performed. The experimental results show that concepts that existed in both laboratory databases were always correctly recognized as candidate matches.(cont.) In addition, concepts which existed in only one database could often be matched with more "generalized" concepts in the other database that could still provide useful information. The architecture of MEDIATE offers advantages in system scalability and robustness. Since concept matching is performed automatically, the only work required to enable data exchange is construction of the semantic network representation. No pre-negotiation is required between institutions to identify data that is compatible for exchange, and there is no additional overhead to add more databases to the exchange network. Because the concept matching occurs dynamically at the time of information exchange, the system is robust to modifications in the underlying native databases as long as the semantic network representations are appropriately updated.by Yao Sun.Ph.D