Johann A. Briffa

An Improved Decoding Algorithm for the Davey-MacKay Construction

The Deletion-Insertion Correcting Code construction proposed by Davey and MacKay consists of an inner code that recovers synchronization and an outer code that provides substitution error protection. The inner code uses low-weight codewords which are added (modulo two) to a pilot sequence. The receiver is able to synchronise on the pilot sequence in spite of the changes introduced by the added codeword. The original bit-level formulation of the inner decoder assumes that all bits in the sparse codebook are identically and independently distributed. Not only is this assumption inaccurate, but it also prevents the use of soft a- priori input to the decoder. We propose an alternative symbol-level inner decoding algorithm that takes the actual codebook into account. Simulation results show that the proposed algorithm has an improved performance with only a small penalty in complexity, and it allows other improvements using inner codes with larger minimum distance.

Evolutionary Multiobjective Image Feature Extraction in the Presence of Noise

A Pareto-based evolutionary multiobjective approach is adopted to optimize the functionals in the trace transform (TT) for extracting image features that are robust to noise and invariant to geometric deformations such as rotation, scale, and translation (RST). To this end, sample images with noise and with RST distortion are employed in the evolutionary optimization of the TT, which is termed evolutionary TT with noise (ETTN). Experimental studies on a fish image database and the Columbia COIL-20 image database show that the ETTN optimized on a few low-resolution images from the fish database can extract robust and RST invariant features from the standard images in the fish database as well as in the COIL-20 database. These results demonstrate that the proposed ETTN is very promising in that it is computationally efficient, invariant to RST deformation, robust to noise, and generalizable.

Formal security analysis of NFC M-coupon protocols using Casper/FDR

Near field communication (NFC) is a standard-based, radio frequency (RF), wireless communication technology that allows data to be exchanged between devices that are less than 10 cm apart. NFC security protocols require formal security analysis before massive adoptions, in order to check whether these protocols meet its requirements and goals. In this paper we formally analyse NFC-based mobile coupon protocols using formal methods (Casper/FDR). We find an attack against the advanced protocol, and then we provide a solution that addresses the vulnerability formally.

Improvement of the Davey-MacKay construction

The Davey-MacKay construction is a deletion-insertion correcting code scheme consisting of an inner code that functions as a pilot sequence to which the receiver seeks to synchronize, and an outer code that provides error protection. We analyse the performance of the inner code in isolation, arguing that these codes provide unequal protection, and demonstrate empirically that the error rate is dependent on the date symbol values. We also propose modifications to the code construction that alleviate this asymmetry. Simulation results show that these codes have an improved performance with no penalty.

Non-binary turbo codes and applications

In this paper we consider the use of q-ary turbo codes on abstract q-ary channels. Simulations show that our 16-ary codes perform well on the q-ary symmetric channel. This validates their suitability as outer codes in non-binary applications. We also compare with the performance of same codes on conventional PSK and QAM modulation, and demonstrate an application of these codes to the insertion/deletion channel.

Codebook and marker sequence design for synchronization-correcting codes

We propose a construction based on synchronization and error-correcting block codes and a matched marker sequence. The block codes can correct insertion, deletion and substitution errors within each codeword. The marker sequence allows the decoder to maintain synchronization at codeword boundaries even at high error rates. An upper bound is given for the performance of these codes over a channel with random substitutions and synchronization errors. It is shown that the performance is largely dependent on the codes minimum Levenshtein distance. The performance of these codes is verified by simulation and compared to published results. In concatenation with a non-binary outer code we obtain a significant improvement in frame error rate at similar overall code rates.

A GPU Implementation of a MAP Decoder for Synchronization Error Correcting Codes

In this paper we present a parallel implementation of a MAP decoder for synchronization error correcting codes. For a modest implementation effort, we demonstrate a considerable decoding speedup, up to two orders of magnitude even on consumer GPUs. This enables the analysis of much larger codes and worse channel conditions than previously possible, and makes applications of such codes feasible for software implementations.

Conditional Probability Based Steganalysis for JPEG Steganography

Inspired by works on the Markov process based steganalysis, we propose a new steganalysis technique based on the conditional probability statistics. Specifically we focus on its performance against the F5 software. In our experiment, we prove that the proposed technique works as well or better than the Markov process based technique in terms of classification accuracy on F5. Our main advantage is a much better computational efficiency. With different number of messages embedded, it can also be seen that the performance of steganalyis depends on the message size embedded. This paper includes the introduction to conditional probability features, how the experiment works, and the discussion of the results.

Classifier ensembles for image identification using multi-objective Pareto features

In this paper we propose classifier ensembles that use multiple Pareto image features for invariant image identification. Different from traditional ensembles that focus on enhancing diversity by generating diverse base classifiers, the proposed method takes advantage of the diversity inherent in the Pareto features extracted using a multi-objective evolutionary Trace transform algorithm. Two variants of the proposed approach have been implemented, one using multilayer perceptron neural networks as base classifiers and the other k-Nearest Neighbor. Empirical results on a large number of images from the Fish-94 and COIL-20 datasets show that on average, the proposed ensembles using multiple Pareto features perform much better than both, the traditional classifier ensembles of single Pareto features with data randomization, and the well-known Random Forest ensemble. The better classification performance of the proposed ensemble is further supported by diversity analysis using a number of measures, indicating that the proposed ensemble consistently produces a higher degree of diversity than traditional ones. Our experimental results demonstrate that the proposed classifier ensembles are robust to various geometric transformations in images such as rotation, scale and translation, and to additive noise.

Evolutionary multi-objective optimization of trace transform for invariant feature extraction

Trace transform is one representation of images that uses different functionals applied on the image function. When the functional is integral, it becomes identical to the well-known Radon transform, which is a useful tool in computed tomography medical imaging. The key question in Trace transform is to select the best combination of the Trace functionals to produce the optimal triple feature, which is a challenging task. In this paper, we adopt a multi-objective evolutionary algorithm adapted from the elitist non-dominated sorting genetic algorithm (NSGA-II), an evolutionary algorithm that has shown to be very efficient for multi-objective optimization, to select the best functionals as well as the optimal number of projections used in Trace transform to achieve invariant image identification. This is achieved by minimizing the within-class variance and maximizing the between-class variance. To enhance the computational efficiency, the Trace parameters are calculated offline and stored, which are then used to calculate the triple features in the evolutionary optimization. The proposed Evolutionary Trace Transform (ETT) is empirically evaluated on various images from fish database. It is shown that the proposed algorithm is very promising in that it is computationally efficient and considerably outperforms existing methods in literatures.