Information-Theoretic Multiclass Classification Based on Binary Classifiers: On Coding Matrix Design, Reliability and Maximum Number of Classes

In this paper, we consider the multiclass classification problem based on sets of independent binary classifiers. Each binary classifier represents the output of a quantized projection of training data onto a randomly generated orthonormal basis vector thus producing a binary label. The ensemble of all binary labels forms an analogue of a coding matrix. The properties of such kind of matrices and their impact on the maximum number of uniquely distinguishable classes are analyzed in this paper from an information-theoretic point of view. We also consider a concept of reliability for such kind of coding matrix generation that can be an alternative to other adaptive training techniques and investigate the impact on the bit error probability. We demonstrate that it is equivalent to the considered random coding matrix without any bit reliability information in terms of recognition rat

Physical object protection based on digital micro-structure fingerprinting

Physical objects typically have, at the surface, a structure that is visible at the microscopic level, known as a microstructure. This structure is usually random and unique for each physical object of particular type, which allows the identification or authentication of such object. These primitives can be used to construct anti-counterfeiting systems. The thesis contains both a theoretical and practical part. The theoretical contributions consist of models of anti-counterfeiting systems, frameworks for the evaluation and pioneers the analysis of attacks against identification and authentication systems. The practical part consists of a large-scale analysis of cardboard microstructures

Fast identification of highly distorted images

In this paper, we consider a low complexity identification system for highly distorted images. The performance of the proposed identification system is analyzed based on the average probability of error. An expected improvement of the performance is obtained combining random projection transform and concept of bit reliability. Simulations based on synthetic and real data confirm the efficiency of the proposed approach

BINARY ROBUST HASHING BASED ON PROBABILISTIC BIT RELIABLITY

In this paper, we consider robust hashing based on a bit reliability function that allows to enhance the performance in terms of both average probability of error and identification complexity. The obtained results demonstrate the high efficiency of the prosed approach. 1

ACTIVE CONTENT FINGERPRINTING: SHRINKAGE AND LATTICE BASED MODULATIONS

In this paper, we extend a new framework introduced as active content fingerprinting in [1] 1 that takes the best from the two worlds of content fingerprinting and digital watermarking to overcome some of the fundamental restrictions of these techniques in terms of performance and complexity. In the proposed framework, contents are modified in a way similar to watermarking to extract more robust fingerprints in contrast to conventional content fingerprinting. We investigate the performance of two modulation techniques based on unidimensional shrinkage and multidimensional lattice quantization. The simulation results on real images demonstrate the high efficiency of the proposed methods facing low-quality compression and additive noise. Index Terms — Content identification, content fingerprinting, watermarking, lattice quantization/decoding

Conception and limits of robust perceptual hashing: toward side information assisted hash functions

In this paper, we consider some basic concepts behind the design of existing robust perceptual hashing techniques for content identification. We show the limits of robust hashing from the communication perspectives as well as propose an approach capable to overcome these shortcomings in certain setups. The consideration is based on both achievable rate and probability of error. We use a fact that most of robust hashing algorithms are based on dimensionality reduction using random projections and quantization. Therefore, we demonstrate the corresponding achievable rate and probability of error based on the random projections and compare with the results for the direct domain. The effect of dimensionality reduction is studied and the corresponding approximations are provided based on Johnson-Lindenstrauss lemma. A side information assisted robust perceptual hashing is proposed as a solution to the above shortcomings. Notations: We use capital letters to denote scalar random variables X and X to denote vector random variables, corresponding small letters x and x to denote the realizations of scalar and vector random variables, respectively. All vectors without sign tilde are assumed to be of the length N and with the sign tilde of length L with the corresponding subindexes. The binary representation of vectors will be denoted as bx with the corresponding subindexing. We use X ∼ pX(x) or simply X ∼ p(x) to indicate that a random variable X is distributed according to pX(x). N(µ,σ2 X) stands for Gaussian distribution with mean µ and variance σ2 X. ||.|| denotes Euclidean vector norm and Q(.) stands for Q-function