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This tutorial paper reviews the theory and design of codes for hiding or embedding information in signals such as images, video, audio, graphics, and text. Such codes have also been called watermarking codes; they can be used in a variety of applications, including copyright protection for digital media, content authentication, media forensics, data binding, and covert communications. Some of these applications imply the presence of an adversary attempting to disrupt the transmission of information to the receiver; other applications involve a noisy, generally unknown, communication channel. Our focus is on the mathematical models, fundamental principles, and code design techniques that are applicable to data hiding. The approach draws from basic concepts in information theory, coding theory, game theory, and signal processing, and is illustrated with applications to the problem of hiding data in images. Keywords—Coding theory, data hiding, game theory, image processing, information theory, security, signal processing, watermarking. I.

This paper introduces JitterBugs, a class of inline interception mechanisms that covertly transmit data by perturbing the timing of input events likely to affect externally observable network traffic. JitterBugs positioned at input devices deep within the trusted environment (e.g., hidden in cables or connectors) can leak sensitive data without compromising the host or its software. In particular, we show a practical Keyboard JitterBug that solves the data exfiltration problem for keystroke loggers by leaking captured passwords through small variations in the precise times at which keyboard events are delivered to the host. Whenever an interactive communication application (such as SSH, Telnet, instant messaging, etc) is running, a receiver monitoring the host’s network traffic can recover the leaked data, even when the session or link is encrypted. Our experiments suggest that simple Keyboard JitterBugs can be a practical technique for capturing and exfiltrating typed secrets under conventional OSes and interactive network applications, even when the receiver is many hops away on the Internet. 1

Information-theoretic analyses for data hiding prescribe embedding the hidden data in the choice of quantizer for the host data. In this paper, we consider a suboptimal implementation of this prescription, with a view to hiding high volumes of data in images with low perceptual degradation. Our two main findings are as follows: (a) In order to limit perceptual distortion while hiding large amounts of data, the hiding scheme must use perceptual criteria in addition to information-theoretic guidelines.

We study information transmission through a finite buffer queue. We model the channel as a finite-state channel whose state is given by the buffer occupancy upon packet arrival; a loss occurs when a packet arrives to a full queue. We study this problem in two contexts; one where the state of the buffer is known at the receiver, and the other where it is unknown. In the former case, we show that the capacity of the channel depends on the long-term loss probability of the buffer. Thus, even though the channel itself has memory, the capacity depends only on the stationary loss probability of the buffer. The main focus of this paper is on the latter case. When the receiver does not know the buffer state, this leads to the study of deletion channels, where symbols are randomly dropped and a subsequence of the transmitted symbols is received. In deletion channels, unlike erasure channels, there is no side-information about which symbols are dropped. We study the achievable rate for deletion channels, and focus our attention on simple (mismatched) decoding schemes. We show that even with simple decoding schemes, with i.i.d. input codebooks, the achievable rate in deletion channels differs from that of erasure channels by at most H0(pd) − pd log K

: We compare the performance of two codes for channels with insertion and deletion errors: marker codes [1] and watermark codes [2], [3]. Both codes are decoded using the sum-product algorithm. Marker codes can be viewed as irregular watermark codes. Our experiments give evidence that irregular constructions can improve watermark codes' performance. Keywords: Levenshtein distance, synchronization error, sum-product algorithm, Gallager code. 1. Introduction Insertion-deletion channels are channels with synchronization problems. Bits are lost and gained between the source and the receiver at unknown positions in the bit stream. Examples of channels that might be modelled as insertion-deletion channels include: Serial line: The clock speed of the transmitter may not be accurately known (for instance due to temperature variations in the clock) and hence the time of arrival of each transmitted bit is not known. Hard disc: Variations in the rotation speed of the platter cause the bit rate...

Information-theoretic analyses for data hiding prescribe embedding the hidden data in the choice of quantizer for the host data. In this paper, we consider a suboptimal implementation of this prescription, with a view to hiding high volumes of data in images with low perceptual degradation. The three main findings are as follows: (i) Scalar quantization based data hiding schemes incur a 2dB penalty from the optimal embedding strategy, which involves vector quantization of the host. (ii) In order to limit perceivable distortion while hiding large amounts of data, hiding schemes must use local perceptual criteria in addition to information-theoretic guidelines. (iii) Powerful erasures and errors correcting codes provide a flexible framework that allows the data-hider freedom of choice of where to embed without requiring synchronization between encoder and decoder.

Information-theoretic analyses for data hiding prescribe embedding the hidden data in the choice of quantizer for the host data. In this paper, we propose practical realizations of this prescription for data hiding in images, with a view to hiding large volumes of data with low perceptual degradation. The hidden data can be recovered reliably under attacks, such as compression and limited amounts of image tampering and image resizing. The three main findings are as follows. 1) In order to limit perceivable distortion while hiding large amounts of data, hiding schemes must use image-adaptive criteria in addition to statistical criteria based on information theory. 2) The use of local criteria to choose where to hide data can potentially cause desynchronization of the encoder and decoder. This synchronization problem is solved by the use of powerful, but simple-to-implement, erasures and errors correcting codes, which also provide robustness against a variety of attacks. 3) For simplicity, scalar quantization-based hiding is employed, even though information-theoretic guidelines prescribe vector quantization-based methods. However, an information-theoretic analysis for an idealized model is provided to show that scalar quantization-based hiding incurs approximately only a 2-dB penalty in terms of resilience to attack.

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The creation of sparse data blocks of fixed size is studied and new algorithm introduced based on constant weight codes. The use of sparse data blocks on multiuser noisy channels (with noise models similar to CDMA and optical WDM) is studied.