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Information Forensics and Security, IEEE Transactions on

Issue 9 • Date Sept. 2013

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Displaying Results 1 - 22 of 22
  • [Front cover]

    Page(s): C1
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  • IEEE Transactions on Information Forensics and Security publication information

    Page(s): C2
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  • Table of contents

    Page(s): 1413 - 1414
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  • Table of contents

    Page(s): 1415 - 1416
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  • “Seeing” ENF: Power-Signature-Based Timestamp for Digital Multimedia via Optical Sensing and Signal Processing

    Page(s): 1417 - 1432
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2687 KB) |  | HTML iconHTML  

    Electric Network Frequency (ENF) fluctuates slightly over time from its nominal value of 50 Hz/60 Hz. The fluctuations in ENF remain consistent across the entire power grid including when measured at physically distant geographical locations. The light intensity from such indoor lighting as fluorescent lamps and incandescent bulbs, which are connected to the power mains, varies in accordance with the ENF, and the fluctuations can be recorded using visual sensors. In this paper, mechanisms using optical sensors and video cameras to record and validate the presence of ENF fluctuations in indoor lighting are presented. Spectrogram and subspace-based signal processing techniques are applied to such recordings to extract the ENF signal by estimating its instantaneous frequencies as a function of time. A high correlation is observed between the ENF fluctuations obtained from indoor lighting and that of the ENF signal captured directly from the power mains supply. A similar mechanism is then used to demonstrate the presence of ENF signals in video recordings taken in different geographical areas. Experimental results show that ENF signals are present in visual recordings made in different geographical areas and can be used as a natural timestamp for optical sensor recordings and video surveillance recordings conducted in indoor lighting environments. Robustness of ENF fluctuation traces under strong compression and CMOS rolling shutter cameras is discussed. Applications of the ENF signal analysis to tampering detection of surveillance video recordings and forensic binding of the audio and visual track of a video are also demonstrated. An analytical model based on an autoregressive process is also developed for ENF signals, and the effectiveness of using innovation sequences from the model for timestamp verification is demonstrated. View full abstract»

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  • Analysis of Reusability of Secure Sketches and Fuzzy Extractors

    Page(s): 1433 - 1445
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2785 KB) |  | HTML iconHTML  

    Secure sketches and fuzzy extractors enable the use of biometric data in cryptographic applications by correcting errors in noisy biometric readings and producing cryptographic materials suitable for authentication, encryption, and other purposes. Such constructions work by producing a public sketch, which is later used to reproduce the original biometric and all derived information exactly from a noisy biometric reading. It has been previously shown that release of multiple sketches associated with a single biometric presents security problems for certain constructions. We continue the analysis to demonstrate that all other constructions are also prone to similar problems and cannot be safely reused even in the presence of very weak adversaries. View full abstract»

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  • Distal-Interphalangeal-Crease-Based User Authentication System

    Page(s): 1446 - 1455
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2618 KB) |  | HTML iconHTML  

    Touchless-based fingerprint recognition technology is thought to be an alternative to touch-based systems to solve problems of hygienic, latent fingerprints, and maintenance. However, there are few studies about touchless fingerprint recognition systems due to the lack of a large database and the intrinsic drawback of low ridge-valley contrast of touchless fingerprint images. This paper proposes an end-to-end solution for user authentication systems based on touchless fingerprint images in which a multiview strategy is adopted to collect images and the robust fingerprint feature of touchless image is extracted for matching with high recognition accuracy. More specifically, a touchless multiview fingerprint capture device is designed to generate three views of raw images followed by preprocessing steps including region of interest (ROI) extraction and image correction. The distal interphalangeal crease (DIP)-based feature is then extracted and matched to recognize the human's identity in which part selection is introduced to improve matching efficiency. Experiments are conducted on two sessions of touchless multiview fingerprint image database with 541 fingers acquired about two weeks apart. An EER of ~ 1.7% can be achieved by using the proposed DIP-based feature, which is much better than touchless fingerprint recognition by using scale invariant feature transformation (SIFT) and minutiae features. The given fusion results show that it is effective to combine the DIP-based feature, minutiae, and SIFT feature for touchless fingerprint recognition systems. The EER is as low as ~ 0.5%. View full abstract»

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  • Robust Median Filtering Forensics Using an Autoregressive Model

    Page(s): 1456 - 1468
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2719 KB) |  | HTML iconHTML  

    In order to verify the authenticity of digital images, researchers have begun developing digital forensic techniques to identify image editing. One editing operation that has recently received increased attention is median filtering. While several median filtering detection techniques have recently been developed, their performance is degraded by JPEG compression. These techniques suffer similar degradations in performance when a small window of the image is analyzed, as is done in localized filtering or cut-and-paste detection, rather than the image as a whole. In this paper, we propose a new, robust median filtering forensic technique. It operates by analyzing the statistical properties of the median filter residual (MFR), which we define as the difference between an image in question and a median filtered version of itself. To capture the statistical properties of the MFR, we fit it to an autoregressive (AR) model. We then use the AR coefficients as features for median filter detection. We test the effectiveness of our proposed median filter detection techniques through a series of experiments. These results show that our proposed forensic technique can achieve important performance gains over existing methods, particularly at low false-positive rates, with a very small dimension of features. View full abstract»

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  • Unicasting on the Secrecy Graph

    Page(s): 1469 - 1481
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3197 KB) |  | HTML iconHTML  

    We consider the secrecy capacity of unicast channels of ad hoc networks exposed to randomly located eavesdroppers, as modeled by S-Graphs. Expressions that quantify the impact of fading and of the density of legitimate nodes relative to that of eavesdroppers are obtained, in terms of the probability that secrecy capacities of unicast channels are nonzero. The results indicate that depending on the relative density of eavesdroppers and the fading intensity, the secrecy capacity of unicast channels subject to fading may be higher than under additive white Gaussian noise (AWGN). View full abstract»

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  • Capacity results and super-activation for wiretap channels with active wiretappers

    Page(s): 1482 - 1496
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4101 KB) |  | HTML iconHTML  

    The classical wiretap channel models secure communication in the presence of a nonlegitimate wiretapper who has to be kept ignorant. Traditionally, the wiretapper is passive in the sense that he only tries to eavesdrop the communication using his received channel output. In this paper, more powerful active wiretappers are studied. In addition to eavesdropping, these wiretappers are able to influence the communication conditions of all users by controlling the corresponding channel states. Since legitimate transmitters and receivers do not know the actual channel realization or the wiretapper's strategy of influencing the channel states, they are confronted with arbitrarily varying channel (AVC) conditions. The corresponding secure communication scenario is, therefore, given by the arbitrarily varying wiretap channel (AVWC). In the context of AVCs, common randomness (CR) has been shown to be an important resource for establishing reliable communication, in particular, if the AVC is symmetrizable. But availability of CR also affects the strategy space of an active wiretapper as he may or may not exploit the common randomness for selecting the channel states. Several secrecy capacity results are derived for the AVWC. In particular, the CR-assisted secrecy capacity of the AVWC with an active wiretapper exploiting CR is established and analyzed in detail. Finally, it is demonstrated for active wiretappers how two orthogonal AVWCs, each useless for transmission of secure messages, can be super-activated to a useful channel allowing for secure communication at nonzero secrecy rates. To the best of our knowledge, this is not possible for passive wiretappers and, further, provides the first example of such super-activation, which has been expected to appear only in the area of quantum communication. Such knowledge is particularly important as it provides valuable insights for the design and the medium access control of future wireless communication systems. View full abstract»

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  • Trusted Collaborative Spectrum Sensing for Mobile Cognitive Radio Networks

    Page(s): 1497 - 1507
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2207 KB) |  | HTML iconHTML  

    Collaborative spectrum sensing is a key technology in cognitive radio networks (CRNs). Although mobility is an inherent property of wireless networks, there has been no prior work studying the performance of collaborative spectrum sensing under attacks in mobile CRNs. Existing solutions based on user trust for secure collaborative spectrum sensing cannot be applied to mobile scenarios, since they do not consider the location diversity of the network, thus over penalize honest users who are at bad locations with severe path-loss. In this paper, we propose to use two trust parameters, location reliability and malicious intention (LRMI), to improve both malicious user detection and primary user detection in mobile CRNs under attack. Location reliability reflects path-loss characteristics of the wireless channel and malicious intention captures the true intention of secondary users, respectively. We propose a primary user detection method based on location reliability (LR) and a malicious user detection method based on LR and Dempster-Shafer (D-S) theory. Simulations show that mobility helps train location reliability and detect malicious users based on our methods. Our proposed detection mechanisms based on LRMI significantly outperforms existing solutions. In comparison to the existing solutions, we show an improvement of malicious user detection rate by 3 times and primary user detection rate by 20% at false alarm rate of 5%, respectively. View full abstract»

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  • On Establishing Edge Adaptive Grid for Bilevel Image Data Hiding

    Page(s): 1508 - 1518
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    We propose, in this paper, a novel edge-adaptive data hiding method for authenticating binary host images. Through establishing a dense edge-adaptive grid (EAG) along the object contours, we use a simple binary image to show that EAG more efficiently selects good data carrying pixel locations (DCPL) associated with “ l-shaped” patterns than block-based methods. Our method employs a dynamic system structure with the redesigned fundamental content adaptive processes (CAP) switch to iteratively trace new contour segments and to search for new DCPLs. By maintaining and updating a location status map, a protective mechanism is proposed to preserve the context of each CAP and their corresponding outcomes. We prove that our method is robust against the interferences caused by close-by contours, image noises, and invariantly selects the same sequence of DCPLs for an arbitrary binary host image and its various marked versions. Comparison shows that our method achieves a good tradeoff between large payload and minimal visual distortion as compared with several classic prior arts for diverse types of binary host images. Moreover, our method well supports state-of-the-art hybrid authentication that integrates data hiding and modern cryptographic techniques. View full abstract»

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  • Sceadan: Using Concatenated N-Gram Vectors for Improved File and Data Type Classification

    Page(s): 1519 - 1530
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1771 KB) |  | HTML iconHTML  

    Over 20 studies have been published in the past decade involving file and data type classification for digital forensics and information security applications. Methods using n-grams as inputs have proven the most successful across a wide variety of types; however, there are mixed results regarding the utility of unigrams and bigrams as inputs independently. In this study, we use support vector machines (SVMs) consisting of unigrams and bigrams, as well as complexity and other byte frequency-based measures, as inputs. Using concatenated unigrams and bigrams as input and a linear kernel SVM, we achieve significantly improved results over those previously reported (73.4% classification rate across 38 file and data types). We are the first to use concatenated n-grams as the sole input, and we show their superiority over inputs used previously. We also found that too many different types of features as inputs result in overfitting and poor generalization properties. We include several types seldom or not studied in the past (Microsoft Office 2010 files, file system data, base64, base85, URL encoding, flash video, M4A, MP4, WMV, and JSON records). The “winning” approach is instantiated in an open source software tool called Sceadan - Systematic Classification Engine for Advanced Data ANalysis. View full abstract»

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  • Blind Verification of Digital Image Originality: A Statistical Approach

    Page(s): 1531 - 1540
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1713 KB) |  | HTML iconHTML  

    Many methods for verifying the integrity of digital images employ various fingerprints associated with acquisition devices. Data on an acquisition device and fingerprints are extracted from an image and confronted with a reference data set that includes all possible fingerprints of the acquisition device. This allows us to draw a conclusion whether the digital image has been modified or not. Thus it is critical to have a sufficiently large, reliable, and true reference data set, otherwise critical miscalculations can arise. Reference data sets are extracted from image data sets that in turn are collected from unknown and nonguaranteed environments (mostly from the Internet). Since often software modifications leave no obvious traces in the image file (e.g., in metadata), it is not easy to recognize original images, from which fingerprints of acquisition devices can be extracted to form true reference data sets. This is the problem addressed in this paper. Given a database consisting of “unguaranteed” images, we introduce a statistical approach for assessing image originality by using the image file's header information (e.g., JPEG compression parameters). First a general framework is introduced. Then the framework is applied to several fingerprint types selected for image integrity verification. View full abstract»

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  • Generalized Random Grid and Its Applications in Visual Cryptography

    Page(s): 1541 - 1553
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (8760 KB) |  | HTML iconHTML  

    Random grid (RG) is a method to implement visual cryptography (VC) without pixel expansion. However, a reconstructed secret with lower visual quality reveals in RG-based VC due to the fact that average light transmission of a share is fixed at 1/2. In this work, we introduce the concept of generalized RG, where the light transmission of a share becomes adjustable, and adopt generalized RG to implement different VC schemes. First, a basic algorithm, a (2,2) generalized RG-based VC, is devised. Based on the (2,2) scheme, two VC schemes including a (2,n) generalized RG-based VC and a (n,n) xor-based meaningful VC are constructed. The two derived algorithms are designed to solve different problems in VC. In the (2,n) scheme, recovered image quality is further improved. In the (n,n) method, meaningful shares are constructed so that the management of shadows becomes more efficient, and the chance of suspicion on secret image encryption is reduced. Moreover, superior visual quality of both the shares and recovered secret image is achieved. Theoretical analysis and experimental results are provided as well, demonstrating the effectiveness and advantages of the proposed algorithms. View full abstract»

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  • [Blank page]

    Page(s): B1554
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  • IEEE Transactions on Information Forensics and Security Edics

    Page(s): 1555
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  • IEEE Transactions on Information Forensics and Security information for authors

    Page(s): 1556 - 1557
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  • Special issue on visual signal processing for wireless networks

    Page(s): 1558
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  • Special Issue on Signal Process for Situational Awareness from Networked Sensors and Social Media

    Page(s): 1559
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  • IEEE Signal Processing Society Information

    Page(s): C3
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  • [Blank page - back cover]

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Aims & Scope

The IEEE Transactions on Information Forensics and Security covers the sciences, technologies, and applications relating to information forensics, information security, biometrics, surveillance and systems applications that incorporate these features.

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Meet Our Editors

Editor-in-Chief
Mauro Barni
University of Siena, Italy