Félix Balado

Performance Analysis of Robust Audio Hashing

We present a novel theoretical analysis of the Philips audio fingerprinting method proposed by Haitsma, Kalker, and Oostveen (2001). Although this robust hashing algorithm exhibits very good performance, the method has only been partially analyzed in the literature. Hence, there is a clear need for a more complete analysis which allows both performance prediction and systematic optimization. We examine here the theoretical performance of the method for Gaussian inputs by means of a statistical model. Our analysis relies on formulating the unquantized fingerprint as a quadratic form, which affords a systematic way to compute the model parameters. We provide closed-form analytical upperbounds for the probability of bit error of the hash for two relevant scenarios: noise addition and desynchronization. We show that these results are useful when applied to real audio signals

Joint iterative decoding and estimation for side-informed data hiding

We present a previously unavailable study on a general procedure for joint iterative decoding and estimation of attack parameters in side-informed data hiding. This type of approach, which exploits iteratively decodable codes for channel identification purposes, has recently become a relevant research trend in many digital communications problems. An advantage is that estimation pilots are not strictly required, thus affording in principle the implementation of blind methods that are able to work close to the theoretically maximum achievable rate. Such a target naturally requires the use of both near-optimum side-informed data hiding methods (e.g., DC-DM) and near-optimum iteratively decodable channel codes (e.g., turbo codes). The attack channels considered in this study are additive independent random noise, amplitude scaling, and a particular case of fine desynchronization of the sampling grid, whose parameters are estimated by maximum likelihood at the decoder. The complexity of this task is tackled by means of the Expectation-Maximization (EM) algorithm, relying on the use of a priori probabilities produced by the iterative decoding process.

BioCode: Two biologically compatible Algorithms for embedding data in non-coding and coding regions of DNA

BackgroundIn recent times, the application of deoxyribonucleic acid (DNA) has diversified with the emergence of fields such as DNA computing and DNA data embedding. DNA data embedding, also known as DNA watermarking or DNA steganography, aims to develop robust algorithms for encoding non-genetic information in DNA. Inherently DNA is a digital medium whereby the nucleotide bases act as digital symbols, a fact which underpins all bioinformatics techniques, and which also makes trivial information encoding using DNA straightforward. However, the situation is more complex in methods which aim at embedding information in the genomes of living organisms. DNA is susceptible to mutations, which act as a noisy channel from the point of view of information encoded using DNA. This means that the DNA data embedding field is closely related to digital communications. Moreover it is a particularly unique digital communications area, because important biological constraints must be observed by all methods. Many DNA data embedding algorithms have been presented to date, all of which operate in one of two regions: non-coding DNA (ncDNA) or protein-coding DNA (pcDNA).ResultsThis paper proposes two novel DNA data embedding algorithms jointly called BioCode, which operate in ncDNA and pcDNA, respectively, and which comply fully with stricter biological restrictions. Existing methods comply with some elementary biological constraints, such as preserving protein translation in pcDNA. However there exist further biological restrictions which no DNA data embedding methods to date account for. Observing these constraints is key to increasing the biocompatibility and in turn, the robustness of information encoded in DNA.ConclusionThe algorithms encode information in near optimal ways from a coding point of view, as we demonstrate by means of theoretical and empirical (in silico) analyses. Also, they are shown to encode information in a robust way, such that mutations have isolated effects. Furthermore, the preservation of codon statistics, while achieving a near-optimum embedding rate, implies that BioCode pcDNA is also a near-optimum first-order steganographic method.

Capacity of DNA Data Embedding Under Substitution Mutations

A number of methods have been proposed over the last decade for encoding information using deoxyribonucleic acid (DNA), giving rise to the emerging area of DNA data embedding. Since a DNA sequence is conceptually equivalent to a sequence of quaternary symbols (bases), DNA data embedding (diversely called DNA watermarking or DNA steganography) can be seen as a digital communications problem where channel errors are analogous to mutations of DNA bases. Depending on the use of coding or noncoding DNA host sequences, which, respectively, denote DNA segments that can or cannot be translated into proteins, DNA data embedding is essentially a problem of communications with or without side information at the encoder. In this paper, the Shannon capacity of DNA data embedding is obtained for the case in which DNA sequences are subject to substitution mutations modeled using the Kimura model from molecular evolution studies. Inferences are also drawn with respect to the biological implications of some of the results presented.

A framework for soft hashing and its application to robust image hashing

Soft hashing, also known as robust hashing or perceptual hashing, consists of summarising multimedia data, so as to obtain a concise representation called a hash value. There has been an increasing interest in the soft hashing problem recently. Techniques implementing soft hashing intend to mirror the behaviour of cryptographic hashing, when the information to be hashed can be subject to different kinds of distortion. Many heuristic techniques for undertaking soft hashing of images and other multimedia data have been devised. Except for some attempts, a framework giving solid guidelines to solve the problem is largely lacking. We provide one possible approach to undertake the modelling of robust soft hashing, detailing the basic problems involved. We show how some prior schemes partly fit into our model.

On distortion-compensated dither modulation data-hiding with repetition coding

An exhaustive analysis of the distortion-compensated dither modulation (DC-DM) data-hiding method with repetition coding is presented. Two decoding strategies, maximum likelihood lattice decoding and Euclidean distance decoding, are discussed and some simplifications presented. An exact performance analysis in terms of the bit error rate (BER) is given; such an exact analysis is currently not available in the literature. Two methods for computing the exact BER and several approximations and bounds, most of them in closed form, are provided. These approximations are employed to propose two novel improvements on the standard DC-DM method with repetition: the use of a weighted Euclidean distance, with optimizable weights, and a vector form of the distortion compensation parameter. Both account for significant performance improvements. DC-DM is compared with quantization methods in the projected domain, showing worse performance against additive noise attacks but higher robustness to cropping attacks. A performance analysis of DC-DM under coarse quantization that can be specialized to JPEG compression is also supplied. All our results are validated with numerical simulations with both synthetic data and real images.

Repetition Coding as an Effective Error Correction Code for Information Encoded in DNA

The goal of DNA data embedding is to enable robust encoding of non-genetic information in DNA. This field straddles the areas of bioinformatics and digital communications, since DNA mutations can be seen as akin to a noisy channel from the point of view of information encoding. In this paper we present two algorithms which, building on a variant of a method proposed by Yachie et al., rely on repetition coding to effectively counteract the impact that mutations have on an embedded message. The algorithms are designed for resynchronising multiple, originally identical, information encoded DNA sequences, embedded within non-coding DNA (ncDNA) sections of a host genome. They use both BLAST and MUSCLE algorithms to accomplish this. Bit error rates at the decoder are established for mutations rates accumulated over a number of generations of the host organism. The empirical results obtained are compared to a theoretical bound for optimal decoding.

New geometric analysis of spread-spectrum data hiding with repetition coding, with implications for side-informed schemes

In this paper we initially provide a new geometric interpretation of additive and multiplicative spread-spectrum (SS) watermarking with repetition coding and ML decoding. The interpretation gives an intuitive rationale on why the multiplicative scheme performs better in front of additive independent attacks, and it is also used to produce a novel quantitative performance analysis. Furthermore, the geometric considerations which explain the advantages of multiplicative SS with repetition afford the proposal of a novel side-informed STDM-like method, which we name Sphere-hardening Dither Modulation (SHDM). This method is the side-informed counterpart of multiplicative SS with repetition coding, in the same sense that STDM is the side-informed counterpart of additive SS with repetition coding.

ML detection of steganography

Digital steganography is the art of hiding information in multimedia content, such that it remains perceptually and statistically unchanged. The detection of such covert communication is referred to as steganalysis. To date, steganalysis research has focused primarily on either, the extraction of features from a document that are sensitive to the embedding, or the inference of some statistical difference between marked and unmarked objects. In this work, we evaluate the statistical limits of such techniques by developing asymptotically optimal tests (Maximum Likelihood) for a number of side informed embedding schemes. The required probability density functions (pdf) are derived for Dither Modulation (DM) and Distortion-Compensated Dither Modulation (DC-DM/SCS) from an steganalysts point of view. For both embedding techniques, the pdfs are derived in the presence and absence of a secret dither key. The resulting tests are then compared to a robust blind steganalytic test based on feature extraction. The performance of the tests is evaluated using an integral measure and receiver operating characteristic (ROC) curves.

On the Embedding Capacity of DNA Strands under Substitution, Insertion, and Deletion Mutations

A number of methods have been proposed over the last decade for embedding information within deoxyribonucleic acid (DNA). Since a DNA sequence is conceptually equivalent to a unidimensional digital signal, DNA data embedding (diversely called DNA watermarking or DNA steganography) can be seen either as a traditional communications problem or as an instance of communications with side information at the encoder, similar to data hiding. These two cases correspond to the use of noncoding or coding DNA hosts, which, respectively, denote DNA segments that cannot or can be translated into proteins. A limitation of existing DNA data embedding methods is that none of them have been designed according to optimal coding principles. It is not possible either to evaluate how close to optimality these methods are without determining the Shannon capacity of DNA data embedding. This is the main topic studied in this paper, where we consider that DNA sequences may be subject to substitution, insertion, and deletion mutations.