In this paper we discuss a random walk model to characterize the pulse discharge battery process. Several
theoretical results are derived including the mean and variance of an unattended battery-driven sensor lifetime.
Some numerical results are presented.
This paper proposes a nonparametric steganalysis method for quantization index modulation (QIM) based steganography. The proposed steganalysis method uses irregularity (or randomness) in the test-image to distinguish between the cover- and the stego-image. We have shown that plain-quantization (quantization without message embedding) induces regularity in the resulting quantized-image; whereas message embedding using QIM increases irregularity in the resulting QIM-stego image. Approximate entropy, an algorithmic entropy measure, is used to quantify irregularity in the test-image. Simulation results presented in this paper show that the proposed
steganalysis technique can distinguish between the cover- and the stego-image with low false rates (i.e. Pfp < 0.1
& Pfn < 0.07 for dither modulation stego and Pfp < 0.12 & Pfn < 0.002 for QIM-stego).
Proc. SPIE. 6505, Security, Steganography, and Watermarking of Multimedia Contents IX
KEYWORDS: Signal to noise ratio, Statistical analysis, Detection and tracking algorithms, Data modeling, Databases, Data hiding, Interference (communication), Quantization, Network security, Steganalysis
A blind source separation method for steganalysis of linear
additive embedding techniques is presented. The paper formulates
steganalysis as a blind source separation problem -- statistically
separate the host and secret message carrying signals. A
probabilistic model of the source distributions is defined based
on its sparsity. The problem of having fewer observations than the
number of sources is effectively handled exploiting the sparsity
and a maximum a posteriori probability (MAP) estimator is
developed to chose the best estimate of the sources. Experimental
details are provided for steganalysis of a discrete cosine
transform (DCT) domain data embedding technique.
Suppose Alice the information hider wants to send a stego message
to Bob in the presence of Wendy the (passive) warden. Wendy
employs one of n different passive steganalysis detectors to
decide if the data from Alice contains any hidden message before
passing it on to Bob. Suppose Alice can choose from a set of
information hiding schemes and possesses only an incomplete information about the steganalysis strategy choice of Wendy. That
is, suppose Alice only knows an ordering of the probabilities (and not the values themselves), say, p1 ≥ p2 ≥ ... ≥ pn where pj is the probability of Wendy using jth detector. Under this scenario we investigate answers to the following two questions by generalizing a previous result by the author and deriving new ones: (a) how must Alice choose the optimal data hiding strategy subject to risk constraints and (b) what is the maximum safe embedding rate, i.e., maximum message rate that can be embedded without being detected by Wendy? Detailed analysis and numerical results are presented to answer these questions.
We define sequential steganography as those class of embedding
algorithms that hide messages in consecutive (time, spatial or
frequency domain) features of a host signal. This paper presents a
steganalysis method that estimates the secret key used in
sequential steganography. A theory is developed for detecting
abrupt jumps in the statistics of the stego signal during
steganalysis. Stationary and non-stationary host signals with low,
medium and high SNR embedding are considered. A locally most
powerful steganalysis detector for the low SNR case is also
derived. Several techniques to make the steganalysis algorithm
work for non-stationary digital image steganalysis are also
presented. Extensive experimental results are shown to illustrate
the strengths and weaknesses of the proposed steganalysis
We analyze the complexity of the steganography problem and show that the decision version of the problem
is NP-complete through transformation from the Knapsack problem. We also give a pseudo-polynomial time
algorithm to optimally solve the steganography problem. This optimal algorithm can also be applied to image
Steganalysis of sequential steganography is presented in this
paper. Abrupt change in statistics due to sequential embedding is
exploited by the proposed technique. Analytical derivations are
presented for several cases along with experimental results.
Experiments show that the proposed method can be used quite
effectively to detect locations and length of messages embedded
using spread spectrum steganography.
Two fundamental questions in steganography are addressed in this
paper, namely, (a) definition of steganography security and (b)
definition of steganographic capacity. Since the main goal of
steganography is covert communications, we argue that these
definitions must be dependent on the type of steganalysis detector
employed to break the embedding algorithm. We propose new
definitions for security and capacity in the presence of a
steganalyst. The intuition and mathematical notions supporting
these definitions are described. Some numerical examples are also
presented to illustrate the need for this investigation.
Steganalysis techniques attempt to differentiate between stego-objects and cover-objects. In recent work we developed an explicit analytic upper bound for the steganographic capacity of LSB based steganographic techniques for a given false probability of detection. In this paper we look at adaptive steganographic techniques. Adaptive steganographic techniques take explicit steps to escape detection. We explore different techniques that can be used to adapt message embedding to the image content or to a known steganalysis technique. We investigate the advantages of adaptive steganography within an analytical framework. We also give experimental results with a state-of-the-art steganalysis technique demonstrating that adaptive embedding results in a significant number of bits embedded without detection.
A mathematical approach to steganalysis is presented in this paper with linear steganography being the main focus. A mathematically formal definition of steganalysis is given followed by definitions for passive and active steganalysis. The steganalysis problem is formulated as blind system identification and conditions for identifiability (successful steganalysis) are derived. A procedure to systematically exploit any available spatial and temporal diversity information for efficient steganalysis is also discussed. Experimental results are given for steganalysis of Gaussian distributed, spread spectrum image steganography and watermarking. The proposed technique is observed to produce impressive results for a variety of performance measures. Based on the results we conclude that a common belief, namely, spread spectrum steganography/watermarking is secure because of the low strength, noise-like message carrier is not valid anymore within the current context. Therefore, new questions regarding steganography security that differ from the standard information theoretic notion are raised and some answers are provided.
The effect of a partially known watermarking channel, additive noise, and multiple watermarks on the watermarking capacity region is studied. A channel can be partially known because of the following reasons : (a) randomly time-varying channel characteristics, (b) unknown attacks by an adversary, (c) uncertainty due to estimation errors such as in oblivious watermarking techniques. A mathematical model for this scenario is introduced. No assumptions are made regarding the probability distribution of the channel. Lower and upper bounds on the feasible watermarking rate region are derived. It is shown that, in terms of watermarking capacity, it is better to cancel the effect of an interfering watermark than to treat is as noise. As a special case, it is also observed that the capacity estimates based on the popular additive Gaussian noise model tend to either over or under estimate the capacity for a single watermark channel. Numerical results are also presented. Finally, we observe that the proposed mathematical model can be applied to real-life applications such as image/video watermarking. Image processing operations such as scaling, geometrical transformations etc. that distort the image (not just add noise) fall under the proposed mathematical model.
We consider a data hiding channel in this paper that is not perfectly known by the encoder and the decoder. The imperfect knowledge could be due to the channel estimation error, time-varying active adversary etc. A mathematical model for this scenario is proposed. Many important attacks such as scaling, geometrical transformations etc. fall under the proposed mathematical model. Minimal assumptions are made regarding the probability distributions of the data-hiding channel. Lower and upper bounds on the data hiding capacity are derived. It is shown that the popular additive Gaussian noise channel model may not suffice in real-world scenarios; the capacity estimates using the additive Gaussian channel model tend to either over- or under-estimate the capacity under different scenarios. Asymptotic value of the capacity as the signal to noise ratio becomes arbitrarily large is also given. Many existing data hiding capacity estimates are observed to be a special case of the formulas derived in this paper. We also observe that the proposed mathematical model can be applied to real-life applications such as data hiding in image/video. Theoretical results are further explained using numerical values.
We propose for the first time a multiple description framework for oblivious watermarking. Parallels between multiple description source coding and the watermarking are drawn. An information theoretic definition of the problem is given. A spread-spectrum watermarking algorithm for DCT based multiple descriptions is described. Performance of the proposed framework for various attack channels such as additive white Gaussian noise, MPEG compression, and random bit error channels shows that the proposed method performs reasonably well compared to non-oblivious schemes.
In this paper, we propose a sequential probability ratio test based on a two parameter Weibull distribution for IC failures. The shape parameter of the Weibull distribution characterizes the decreasing, constant and the increasing failure rate regions in the bath tub model for ICs. The algorithm detects the operating region of the IC based on the observed failure times. Unlike the fixed-length test, the proposed algorithm due to its sequential nature uses the minimum average number of devices for the test for fixed error tolerances in the detection procedure. We find that the proposed test is on an average 96 percent more efficient than the fixed-length test. Our algorithm is shown to be highly robust to the variations in the model parameters unlike other existing sequential tests. Further, extensive simulations are used to validate the analytic results of the sequential test.