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Forensic analysis of nonlinear collusion attacks for multimedia fingerprinting.

Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Unique identification information is embedded into each distributed copy of multimedia signal and serves as a digital fingerprint. Collusion attack is a cost-effective attack against digital fingerprinting, where colluders combine several copies with the same content but different fingerprints to remove or attenuate the original fingerprints. In this paper, we investigate the average collusion attack and several basic nonlinear collusions on independent Gaussian fingerprints, and study their effectiveness and the impact on the perceptual quality. With unbounded Gaussian fingerprints, perceivable distortion may exist in the fingerprinted copies as well as the copies after the collusion attacks. In order to remove this perceptual distortion, we introduce bounded Gaussian-like fingerprints and study their performance under collusion attacks. We also study several commonly used detection statistics and analyze their performance under collusion attacks. We further propose a preprocessing technique of the extracted fingerprints specifically for collusion scenarios to improve the detection performance.

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Anti-collusion forensics of multimedia fingerprinting using orthogonal modulation.

Digital fingerprinting is a method for protecting digital data in which fingerprints that are embedded in multimedia are capable of identifying unauthorized use of digital content. A powerful attack that can be employed to reduce this tracing capability is collusion, where several users combine their copies of the same content to attenuate/remove the original fingerprints. In this paper, we study the collusion resistance of a fingerprinting system employing Gaussian distributed fingerprints and orthogonal modulation. We introduce the maximum detector and the thresholding detector for colluder identification. We then analyze the collusion resistance of a system to the averaging collusion attack for the performance criteria represented by the probability of a false negative and the probability of a false positive. Lower and upper bounds for the maximum number of colluders K(max) are derived. We then show that the detectors are robust to different collusion attacks. We further study different sets of performance criteria, and our results indicate that attacks based on a few dozen independent copies can confound such a fingerprinting system. We also propose a likelihood-based approach to estimate the number of colluders. Finally, we demonstrate the performance for detecting colluders through experiments using real images.

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Multipurpose image watermarking algorithm based on multistage vector quantization.

The rapid growth of digital multimedia and Internet technologies has made copyright protection, copy protection, and integrity verification three important issues in the digital world. To solve these problems, the digital watermarking technique has been presented and widely researched. Traditional watermarking algorithms are mostly based on discrete transform domains, such as the discrete cosine transform, discrete Fourier transform (DFT), and discrete wavelet transform (DWT). Most of these algorithms are good for only one purpose. Recently, some multipurpose digital watermarking methods have been presented, which can achieve the goal of content authentication and copyright protection simultaneously. However, they are based on DWT or DFT. Lately, several robust watermarking schemes based on vector quantization (VQ) have been presented, but they can only be used for copyright protection. In this paper, we present a novel multipurpose digital image watermarking method based on the multistage vector quantizer structure, which can be applied to image authentication and copyright protection. In the proposed method, the semi-fragile watermark and the robust watermark are embedded in different VQ stages using different techniques, and both of them can be extracted without the original image. Simulation results demonstrate the effectiveness of our algorithm in terms of robustness and fragility.

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Ergodic chaotic parameter modulation with application to digital image watermarking.

This paper presents a novel technique for image watermarking based on chaos theory. Chaotic parameter modulation (CPM) is employed to modulate the copyright information into the bifurcating parameter of a chaotic system. The system output is a wideband signal and is used as a watermark to be inserted into the host image. In the detection, a novel method based on the ergodic property of chaotic signal is developed to demodulate the embedded copyright information. Compared to previous works on blind watermarking, the proposed technique can effectively remove the interference from the host image and, thus, improve the detection performance dramatically. Simulation results show that the ergodic CPM approach is effective for image watermarking in terms of noise performance, robustness against attacks, and payload. In addition, its implementation is very simple and the computation speed is fast. Compared to holographic transform domain method and the conventional spread spectrum watermarking scheme, the proposed technique is shown to be superior.

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Rate-distortion optimal video summary generation.

The need for video summarization originates primarily from a viewing time constraint. A shorter version of the original video sequence is desirable in a number of applications. Clearly, a shorter version is also necessary in applications where storage, communication bandwidth, and/or power are limited. The summarization process inevitably introduces distortion. The amount of summarization distortion is related to its "conciseness," or the number of frames available in the summary. If there are m frames in the original sequence and n frames in the summary, we define the summarization rate as m/n, to characterize this "conciseness". We also develop a new summarization distortion metric and formulate the summarization problem as a rate-distortion optimization problem. Optimal algorithms based on dynamic programming are presented and compared experimentally with heuristic algorithms. Practical constraints, like the maximum number of frames that can be skipped, are also considered in the formulation and solution of the problem.

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Inverse halftoning algorithm using edge-based lookup table approach.

The inverse halftoning algorithm is used to reconstruct a gray image from an input halftone image. Based on the recently published lookup table (LUT) technique, this paper presents a novel edge-based LUT method for inverse halftoning which improves the quality of the reconstructed gray image. The proposed method first uses the LUT-based inverse halftoning method as a preprocessing step to transform the given halftone image to a base gray image, and then the edges are extracted and classified from the base gray image. According to these classified edges, a novel edge-based LUT is built up to reconstruct the gray image. Based on a set of 30 real training images with both low-and high-frequency contents, experimental results demonstrated that the proposed method achieves a better image quality when compared to the currently published two methods, by Chang et al. and Meşe and Vaidyanathan.

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JBEAM: multiscale curve coding via beamlets.

A multiscale coder for curves and boundaries is presented. It utilizes a multiscale structure--beamlets--that is designed primarily for linear and curvilinear features. The coder is composed of three main components: 1) a rate-distortion optimized beamlet-based representation, 2) a tree-based coding from a beamlet representation to a symbol stream, and 3) an entropy coder. This coder is named "JBEAM." Taking advantage of its multiscale property, we utilized tree-based coding to make it progressive. The derived coder has a low order of computational complexity. Simulations demonstrate an advantage over the state-of-the-art industrial standard: JBIG 2. A software package, which includes an implementation of JBEAM, is made available. Variations and potential improvements of this method will be discussed. This work may inspire more activities in this line of research, improving curve coding.

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A 2-D orientation-adaptive prediction filter in lifting structures for image coding.

Lifting-style implementations of wavelets are widely used in image coders. A two-dimensional (2-D) edge adaptive lifting structure, which is similar to Daubechies 5/3 wavelet, is presented. The 2-D prediction filter predicts the value of the next polyphase component according to an edge orientation estimator of the image. Consequently, the prediction domain is allowed to rotate +/-45 degrees in regions with diagonal gradient. The gradient estimator is computationally inexpensive with additional costs of only six subtractions per lifting instruction, and no multiplications are required.

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Context quantization by kernel Fisher discriminant.

Optimal context quantizers for minimum conditional entropy can be constructed by dynamic programming in the probability simplex space. The main difficulty, operationally, is the resulting complex quantizer mapping function in the context space, in which the conditional entropy coding is conducted. To overcome this difficulty, we propose new algorithms for designing context quantizers in the context space based on the multiclass Fisher discriminant and the kernel Fisher discriminant (KFD). In particular, the KFD can describe linearly nonseparable quantizer cells by projecting input context vectors onto a high-dimensional curve, in which these cells become better separable. The new algorithms outperform the previous linear Fisher discriminant method for context quantization. They approach the minimum empirical conditional entropy context quantizer designed in the probability simplex space, but with a practical implementation that employs a simple scalar quantizer mapping function rather than a large lookup table.

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Video coding with fixed-length packetization for a tandem channel.

A robust scheme is presented for the efficient transmission of packet video over a tandem wireless Internet channel. This channel is assumed to have bit errors (due to noise and fading on the wireless portion of the channel) and packet erasures (due to congestion on the wired portion). First, we propose an algorithm to optimally switch between intracoding and intercoding for a video coder that operates on a packet-switched network with fixed-length packets. Different re-synchronization schemes are considered and compared. This optimal mode selection algorithm is integrated with an efficient channel encoder, a cyclic redundancy check outer coder concatenated with an inner rate-compatible punctured convolutional coder. The system performance is both analyzed and simulated. Last, the framework is extended to operate on a time-varying wireless Internet channel with feedback information from the receiver. Both instantaneous feedback and delayed feedback are evaluated, and an improved method of refined distortion estimation for encoding is presented and simulated for the case of delayed feedback.

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VAPOR: variance-aware per-pixel optimal resource allocation.

Characterizing the video quality seen by an end-user is a critical component of any video transmission system. In packet-based communication systems, such as wireless channels or the Internet, packet delivery is not guaranteed. Therefore, from the point-of-view of the transmitter, the distortion at the receiver is a random variable. Traditional approaches have primarily focused on minimizing the expected value of the end-to-end distortion. This paper explores the benefits of accounting for not only the mean, but also the variance of the end-to-end distortion when allocating limited source and channel resources. By accounting for the variance of the distortion, the proposed approach increases the reliability of the system by making it more likely that what the end-user sees, closely resembles the mean end-to-end distortion calculated at the transmitter. Experimental results demonstrate that variance-aware resource allocation can help limit error propagation and is more robust to channel-mismatch than approaches whose goal is to strictly minimize the expected distortion.

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Sparse overcomplete Gabor wavelet representation based on local competitions.

Gabor representations present a number of interesting properties despite the fact that the basis functions are nonorthogonal and provide an overcomplete representation or a nonexact reconstruction. Overcompleteness involves an expansion of the number of coefficients in the transform domain and induces a redundancy that can be further reduced through computational costly iterative algorithms like Matching Pursuit. Here, a biologically plausible algorithm based on competitions between neighboring coefficients is employed for adaptively representing any source image by a selected subset of Gabor functions. This scheme involves a sharper edge localization and a significant reduction of the information redundancy, while, at the same time, the reconstruction quality is preserved. The method is characterized by its biological plausibility and promising results, but it still requires a more in depth theoretical analysis for completing its validation.

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Video halftoning.

This paper studies video halftoning that renders a digital video sequence onto display devices, which have limited intensity resolutions and color palettes, by trading the spatiotemporal resolution for enhanced intensity/color resolution. This trade is needed when a continuous tone video is not necessary or not practical for video display, transmission, and storage. In particular, the quantization error of a pixel is diffused to its spatiotemporal neighbors by separable one-dimensional temporal and two-dimensional spatial error diffusions. Motion-adaptive gain control is employed to enhance the temporal consistency of the visual patterns by minimizing the flickering artifacts. Experimental results of halftone and colortone videos are demonstrated and evaluated with various halftoning techniques.

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Lossless watermarking for image authentication: a new framework and an implementation.

We present a novel framework for lossless (invertible) authentication watermarking, which enables zero-distortion reconstruction of the un-watermarked images upon verification. As opposed to earlier lossless authentication methods that required reconstruction of the original image prior to validation, the new framework allows validation of the watermarked images before recovery of the original image. This reduces computational requirements in situations when either the verification step fails or the zero-distortion reconstruction is not needed. For verified images, integrity of the reconstructed image is ensured by the uniqueness of the reconstruction procedure. The framework also enables public(-key) authentication without granting access to the perfect original and allows for efficient tamper localization. Effectiveness of the framework is demonstrated by implementing the framework using hierarchical image authentication along with lossless generalized-least significant bit data embedding.

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Watermarking mesh-based representations of 3-D objects using local moments.

A new methodology for fingerprinting and watermarking three-dimensional (3-D) graphical objects is proposed in this paper. The 3-D graphical objects are described by means of polygonal meshes. The information to be embedded is provided as a binary code. A watermarking methodology has two stages: embedding and detecting the information that has been embedded in the given media. The information is embedded by means of local geometrical perturbations while maintaining the local connectivity. A neighborhood localized measure is used for selecting appropriate vertices for watermarking. A study is undertaken in order to verify the suitability of this measure for selecting vertices from regions where geometrical perturbations are less perceptible. Two different watermarking algorithms, that do not require the original 3-D graphical object in the detection stage, are proposed. The two algorithms differ with respect to the type of constraint to be embedded in the local structure: by using parallel planes and bounding ellipsoids, respectively. The information capacity of various 3-D meshes is analyzed when using the proposed 3-D watermarking algorithms. The robustness of the 3-D watermarking algorithms is tested to noise perturbation and to object cropping.

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An analysis of the efficiency of different SNR-scalable strategies for video coders.

In this paper, we analyze the efficiency of three signal-to-noise scalable strategies for video coders using single-loop motion-compensated prediction (MCP). In our analysis, we assume the video sequences have uniform and constant translational motion and we model MCP as a stochastic filter. We also assume an exponential model for the distortion-rate function of the intraframe coding. The analysis is divided into two parts: the steady-state analysis and the transient analysis. In the first part, only the steady-state response of the coders is taken into account, and, thus, this analysis allows us to asses approximately the efficiency of coders with long input sequences. The transitory analysis considers both the transient and the steady-state responses of the coders, which makes it appropriate to analyze coders using periodic intraframes or with short input sequences. To validate our analysis, theoretical results have been compared to results from encodings of real video sequences using the scalable adaptive motion compensated wavelet video coder. We show that our theoretical analysis effectively describes qualitatively the main trends of every video coding strategy.

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Reducing video-quality fluctuations for streaming scalable video using unequal error protection, retransmission, and interleaving.

Forward error correction based multiple description (MD-FEC) transcoding for transmitting embedded bitstream over the packet erasure networks has been extensively studied in the past. In the existing work, a single embedded source bitstream, e.g., the bitstream of a group of pictures (GOP) encoded using three-dimensional set partitioning in hierarchical trees is optimally protected unequal error protection (UEP) in the rate-distortion sense. However, most of the previous work on transmitting embedded video using MD-FEC assumed that one GOP is transmitted only once, and did not consider the chance of retransmission. This may lead to noticeable video quality variations due to varying channel conditions. In this paper, a novel window-based packetization scheme is proposed, which combats bursty packet loss by combining the following three techniques: UEP, retransmission, and GOP-level interleaving. In particular, two retransmission mechanisms, namely segment-wise retransmission and byte-wise retransmission, are proposed based on different types of receiver feedback. Moreover, two levels of rate allocations are introduced: intra-GOP rate allocation minimizes the distortion of individual GOP; while inter-GOP rate allocation intends to reduce video quality fluctuations by adaptively allocating bandwidth according to video signal characteristics and client buffer status. In this way, more consistent video quality can be achieved under various packet loss probabilities, as demonstrated by our experimental results.

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A cross-layer diversity technique for multicarrier OFDM multimedia networks.

Diversity can be used to combat multipath fading and improve the performance of wireless multimedia communication systems. In this work, by considering transmission of an embedded bitstream over an orthogonal frequency division multiplexing (OFDM) system in a slowly varying Rayleigh faded environment, we develop a cross-layer diversity technique which takes advantage of both multiple description coding and frequency diversity techniques. More specifically, assuming a frequency-selective channel, we study the packet loss behavior of an OFDM system and construct multiple independent descriptions using an FEC-based strategy. We provide some analysis of this cross-layer approach and demonstrate its superior performance using the set partitioning in hierarchical trees image coder.

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