<![CDATA[ IEEE Transactions on Information Forensics and Security - new TOC ]]>
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TOC Alert for Publication# 10206 2017September21<![CDATA[A New Multimodal Approach for Password Strength Estimation—Part I: Theory and Algorithms]]>1212282928441413<![CDATA[A New Multimodal Approach for Password Strength Estimation—Part II: Experimental Evaluation]]>1212284528602894<![CDATA[Beamforming and Power Splitting Designs for AN-Aided Secure Multi-User MIMO SWIPT Systems]]>1212286128742433<![CDATA[IllusionPIN: Shoulder-Surfing Resistant Authentication Using Hybrid Images]]>1212287528891982<![CDATA[Statistical Detection of JPEG Traces in Digital Images in Uncompressed Formats]]>$8\times 8$ block-Discrete Cosine Transform (DCT) domain. In fact, the distribution of such coefficients is derived theoretically both under the hypotheses of no compression and previous compression with a certain quality factor, allowing for the computation of the respective likelihood functions. Then, two classification tests based on different statistics are proposed, both relying on a discriminative threshold that can be determined without the need of any training phase. The statistical analysis is based on the only assumptions of generalized Gaussian distribution of DCT coefficients and independence among DCT frequencies, thus resulting in robust detectors applying to any uncompressed image. In fact, experiments on different datasets show that the proposed models are suitable for the images of different sizes and source cameras, thus overcoming dataset-dependence issues that typically affect the state-of-art techniques.]]>1212289029053223<![CDATA[Strategic Trust in Cloud-Enabled Cyber-Physical Systems With an Application to Glucose Control]]>FlipIt game. The equilibrium outcome in the signaling game determines the incentives in the FlipIt game. In turn, the equilibrium outcome in the FlipIt game determines the prior probabilities in the signaling game. The Gestalt Nash equilibrium (GNE) characterizes the steady state of the overall macro-game. The novel contributions of this paper include proofs of the existence, uniqueness, and stability of the GNE. We also apply GNEs to strategically design a trust mechanism for a cloud-assisted insulin pump. Without requiring the use of historical data, the GNE obtains a risk threshold beyond which the pump should not trust messages from the cloud. Our framework contributes to a modeling paradigm called games-of-games.]]>1212290629192036<![CDATA[Optimal Download Cost of Private Information Retrieval for Arbitrary Message Length]]>$K$ messages from $N$ non-communicating replicated databases, each of which stores all $K$ messages, without revealing anything (in the information theoretic sense) about the identity of the desired message index to any individual database. If the size of each message is $L$ bits and the total download required by a PIR scheme from all $N$ databases is $D$ bits, then $D$ is called the download cost and the ratio $L/D$ is called an achievable rate. For fixed $K,N\in \mathbb {N}$ , the capacity of PIR, denoted by $C$ , is the supremum of achievable rates over all PIR schemes and over all message sizes, and was recently shown to be $C=(1+1/N+1/N^{2}+\cdots +1/N^{K-1})^{-1}$ . In this paper, for arbitrary $K$ and $N$ , we explore the minimum download cost $D_{L}$ across all PIR schemes (not restricted to linear schemes) for arbitrary message lengths $L$ under arbitrary choices of alphabet (not restricted to finite fields) for the message and download symbols. If the same $M$ -ary alphabet is used for the message and download symbols, then we show that the optimal download cost in $M$ -ary symbols is $D_{L}=\lceil \frac {L}{C}\rceil$ . If the message symbols are in $M$ -ary alphabet and the downloaded symbols are in $M'$ -ary alphabet, then we show that the optimal download cost in $M'$ -ary symbols, $D_{L}\in \{\lceil ~({L'}/{C})\rceil, \lceil ~({L'}/{C}\rceil -1,\lceil ~({L'}/{C})\rceil -2\}$ , where $L'= \lceil L \log _{M'} M\rceil$ , i.e., the optimal download cost is characterized to within two symbols.]]>121229202932268<![CDATA[Security of Cached Content in NDN]]>verification attack, in which a large amount of unverified content is accessed to exhaust system resources. In this paper, we carefully look at the possible concerns of our preliminary work, including verification attack, and present a simple but effective solution. The proposed solution mitigates the weakness of our preliminary work and allows this paper to be deployed for real-world applications.]]>1212293329442314<![CDATA[Crowd Violence Detection Using Global Motion-Compensated Lagrangian Features and Scale-Sensitive Video-Level Representation]]>1212294529565802<![CDATA[Jamming a TDD Point-to-Point Link Using Reciprocity-Based MIMO]]>1212295729701188<![CDATA[SIFT Keypoint Removal via Directed Graph Construction for Color Images]]>scale invariant feature transform (SIFT) has been widely employed in many applications. Recently, the security of SIFT against malicious attack has been attracting increasing attention, and several techniques have been devised to remove SIFT keypoints intentionally. However, most of the existing methods still suffer from the following three problems: 1) the keypoint removal rate achieved by many techniques is unsatisfactory when removing keypoints within multiple octaves; 2) noticeable artifacts are introduced in the processed image, especially in those highly textured regions; and 3) the color information is totally neglected, precluding the widespread adoption of those methods. To tackle these challenges, in this paper, we propose a novel SIFT keypoint removal framework. By modeling the difference of Gaussian space as a directed weighted graph, we derive a set of strict inequality constraints to remove a SIFT keypoint along a pre-constructed acyclic path. To minimize the incurred distortion, the path is strategically designed over the directed graph. Furthermore, we propose a simple yet effective optimization framework for recovering the color information of the keypoint-removed image. Extensive experiments are provided to show the superior performance of our proposed scheme over the state-of-the-art techniques, in both the scenarios of removing keypoints in a single octave and in multiple octaves.]]>1212297129854464<![CDATA[Enabling Central Keyword-Based Semantic Extension Search Over Encrypted Outsourced Data]]>1212298629972089<![CDATA[OTIBAAGKA: A New Security Tool for Cryptographic Mix-Zone Establishment in Vehicular Ad Hoc Networks]]>121229983010820<![CDATA[Double Behavior Characteristics for One-Class Classification Anomaly Detection in Networked Control Systems]]>1212301130232227<![CDATA[Recursive Matrix Oblivious RAM: An ORAM Construction for Constrained Storage Devices]]>1212302430383370<![CDATA[I Know What You Saw Last Minute—Encrypted HTTP Adaptive Video Streaming Title Classification]]>1212303930491864<![CDATA[Localization of Diffusion-Based Inpainting in Digital Images]]>1212305030643854<![CDATA[PRNU-Based Camera Attribution From Multiple Seam-Carved Images]]>$50 \times 50$ pixels. In this paper, we show that given multiple seam-carved images from the same camera, source attribution can still be possible even if the size of uncarved blocks in the image is less than the recommended size of $50 \times 50$ pixels. Theoretical analysis and experiments with multiple cameras demonstrate that the effectiveness of our scheme depends on the number of seams carved from an image and the randomness of the seam positions.]]>1212306530803256<![CDATA[DPPro: Differentially Private High-Dimensional Data Release via Random Projection]]>$(\epsilon,\delta)$ -differential privacy. Based on the theoretical analysis, we observed that the utility guarantees of released data depend on the projection dimension and the variance of the noise. Extensive experimental results demonstrate that DPPro substantially outperforms several state-of-the-art solutions in terms of perturbation error and privacy budget on high-dimensional data sets.]]>1212308130931531