Hong Peng

A learning-based audio watermarking scheme using kernel Fisher discriminant analysis

A novel learning-based audio watermarking scheme using kernel Fisher discriminant analysis (KFDA) is proposed in this paper. Two techniques, down-sampling technique and energy relationship modulation technique, are developed in order to guarantee good fidelity of the watermarked audio signal. At the same time, local energy relationship between audio sub-frames is hid in the watermarked audio signal with watermark embedding. Moreover, a learning-based watermark detector using the KFDA is exploited and it extracts the watermark by learning the local energy relationship hid in the watermarked audio signal. Due to powerful non-linear learning ability and good generalization ability of the KFDA, the learning-based watermark detector can exhibit high robustness against common audio signal processing or attacks compared with other audio watermarking methods. In addition, it also has simple implementation and lower computation complexity.

Audio watermarking scheme robust against desynchronization attacks based on kernel clustering

In this paper, we propose an adaptive audio watermarking scheme based on kernel fuzzy c-means (KFCM) clustering algorithm, which possesses robust ability against common signal processing and desynchronization attacks. The original audio signal is partitioned into audio frames and then each audio frame is further divided as two sub-frames. In order to resist desynchronization attacks, we embed a synchronization code into first sub-frame of each audio frame by using a mean quantization technique in temporal domain. Moreover, watermark signal is hid into DWT coefficients of second sub-frame of each audio frame by using an energy quantization technique. A local audio feature data set extracted from all audio frames is used to train a KFCM. The well-trained KFCM is used to adaptively control quantization steps in above two quantization techniques. The experimental results show the proposed scheme is robust to common signal processing (such as MP3 lossy compression, noise addition, filtering, re-sampling, re-quantizing) and desynchronization attacks (random cropping, pitch shifting, amplitude variation, time-scale modification, jittering).

Spiking neural P systems with multiple channels

Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices.

A Fragile Watermarking Scheme Based on Hash Function for Web Pages

With the development of Internet, the integrity protection of web pages has become an important problem. A fragile watermarking scheme based on hash function for web pages is proposed in this paper. Watermarks are generated by using hash function and these watermarks are related to the web pages content. Then, the watermarks are embedded into the tags and embedding locations, which are randomly pointed. In the detection phase, regenerated watermarks and extracted watermarks are got from the watermarked web page. Finally, according to comparing the mentioned above two kind watermarks, we can judge whether the web page has been tampered or not. In addition, we can also identify the types of modification and locate the locations of modification.

Knowledge representation using fuzzy spiking neural P system

This paper presents a fuzzy spiking neural P system (FSN P system) to represent the fuzzy production rules in a knowledge base of a rule-based system, where the certainty factors of fuzzy production rules and the truth values of propositions are described by trapezoidal fuzzy numbers. In the proposed FSN P system, the definition of traditional neurons has been extended. The neurons are divided into two types: proposition neurons and rule neurons; the content of each neuron is a trapezoidal fuzzy number in [0, 1] instead of an integer. Also the fuzzy reasoning process can be modeled by the proposed FSN P system.

A Fragile Watermarking Scheme Based on SVD for Web Pages

With the development of the Internet, more and more people concern the protection of web pages. Fragile watermarking technique is an effective method for solving the tamper-proof of web pages. In this paper, a fragile watermarking scheme based on singular value decomposition (SVD) for web pages is proposed. First of all, a model utilizing the frequency of letters is used to denote a web page, and the web page is expressed as a matrix through this model. Afterwards, we take SVD transformation on the matrix and extract its singular values to generate watermarks. Finally, the watermarks are embedded into the letters of HTML tags by altering the upper and lower cases of letters, simultaneously, they are embedded into the embedding locations controlled by a secret key. In the detection procedure, whether a web page has been tampered or not can be judged effectively only by comparing the generated watermarks with the extracted watermarks. The experiment shows that the proposed scheme has stronger abilities to identify the types of modifications and to locate the locations of modifications.

A Fragile Watermarking Scheme for Tamper-Proof of Web Pages

With the development of the Internet, getting informationthrough browsing web pages has become a popularway. How to protect the integrity of a web page is anurgent problem. As an effective method, a novel fragilewatermarking scheme for tamper-proof of web pages isproposed in this paper for solving the problem. In ourscheme, the watermarks that are related to the content ofthe original web page are generated based on the SHA-1algorithm, and then they are embedded into the embeddinglocations controlled by two secret keys. In the detectionprocedure, whether a web page has been tampered or notcan be judged effectively only by comparing the generatedwatermarks with the extracted watermarks. The experimentshows that the proposed scheme has stronger abilities tolocate the locations of modifications and to identify the typesof modifications.

Spiking neural P systems with multiple channels and anti-spikes

Spiking neural P systems (SN P systems) with multiple channels are a variant of SN P systems presented recently. By introducing anti-spikes in neurons, SN P systems with multiple channels and anti-spikes are constructed in this work, where both spikes and anti-spikes are used in rules with channel labels. The Turing universality as number generating and accepting devices is proved at first, and then a universal SN P systems with multiple channels and anti-spikes for computing functions is investigated. At last, a small universal system using 65 neurons for computing any Turing computable function is given.

A Fragile Software Watermarking for Tamper-Proof

A fragile software watermarking scheme for integrity verification of software is proposed in this paper. The algorithm uses the idea of semantic-preserving code substitution for embedding the watermark. With the system, the generated watermark relates closely to the content of software, and the scheme has highly sensitive to different types of attacks. Furthermore, both watermark generating controlled by a key and watermark embedding position under the control of another key enhance the security of watermarking scheme. The scheme not only can effectively detect tampering, but also has the ability to identify the type of tampering clearly.

Matrix Representation of Parallel Computation for Spiking Neural P Systems

Spiking neural P systems (in short, SN P systems) is a class of distributed parallel computing models. Parallel computation of matrix operations has been supported on some new computing devices such as GPU, which provides a promising way to simulate the parallel computation of SN P systems. In this paper a matrix representation method of parallel computation for SN P systems is developed. In firing mechanism of SN P systems, the delay factor plays the role of controlling the receiving of spikes in neurons and the opportunity of emitting the spikes after the firing. In order to achieve the parallel computation of SN P systems, several matrices or vectors are introduced to decompose the firing mechanism of neurons. The parallel computation procedure of SN P systems can be achieved by the operations of the matrices or vectors. Two examples are used to illustrate the parallel computation procedure using the matrix operations.