Zhen Yang^1,2, Yongfeng Huang^1,2,*, Xing Li^1,2, Wenyu Wang³

CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 1-17, 2018, DOI:10.3970/cmc.2018.03697

Abstract To cope with privacy leakage caused by multimedia outsourcing and sharing, data provenance is used to analyze leaked multimedia and provide reactive accountability. Existing schemes of multimedia provenance are based on watermarking protocols. In an outsourcing scenario, existing schemes face two severe challenges: 1) when data leakage occurs, there exists a probability that data provenance results can be repudiated, in which case data provenance tracking fails; and 2) when outsourced data are shared, data encryption transfer causes key management burden outside the schemes, and privacy leakage threatens users. In this paper, we propose a novel More >

Changting Shi^{1, *}

CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 635-652, 2018, DOI:10.32604/cmc.2018.03754

Abstract In the last decade, IoT has been widely used in smart cities, autonomous driving and Industry 4.0, which lead to improve efficiency, reliability, security and economic benefits. However, with the rapid development of new technologies, such as cognitive communication, cloud computing, quantum computing and big data, the IoT security is being confronted with a series of new threats and challenges. IoT device identification via Radio Frequency Fingerprinting (RFF) extracting from radio signals is a physical-layer method for IoT security. In physical-layer, RFF is a unique characteristic of IoT device themselves, which can difficultly be tampered. More >

Chengsheng Yuan^1,2,3, Xinting Li³, Q. M. Jonathan Wu³, Jin Li^4,5, Xingming Sun^1,2

CMC-Computers, Materials & Continua, Vol.53, No.4, pp. 357-372, 2017, DOI:10.3970/cmc.2017.053.357

Abstract Fingerprint-spoofing attack often occurs when imposters gain access illegally by using artificial fingerprints, which are made of common fingerprint materials, such as silicon, latex, etc. Thus, to protect our privacy, many fingerprint liveness detection methods are put forward to discriminate fake or true fingerprint. Current work on liveness detection for fingerprint images is focused on the construction of complex handcrafted features, but these methods normally destroy or lose spatial information between pixels. Different from existing methods, convolutional neural network (CNN) can generate high-level semantic representations by learning and concatenating low-level edge and shape features from… More >

K. Y. Volokh^*

Molecular & Cellular Biomechanics, Vol.10, No.2, pp. 107-135, 2013, DOI:10.3970/mcb.2013.010.107

Abstract The application of mechanics to biology – biomechanics – bears great challenges due to the intricacy of living things. Their dynamism, along with the complexity of their mechanical response (which in itself involves complex chemical, electrical, and thermal phenomena) makes it very difficult to correlate empirical data with theoretical models. This difficulty elevates the importance of useful biomechanical theories compared to other fields of engineering. Despite inherent imperfections of all theories, a well formulated theory is crucial in any field of science because it is the basis for interpreting observations. This is all-the-more vital, for… More >

PABLO A. VALDECANTOS, MARTÍN E. ARGAÑARAZ, CARLOS M. ABATE, DORA C. MICELI

BIOCELL, Vol.28, No.3, pp. 287-297, 2004, DOI:10.32604/biocell.2004.28.287

Abstract As a step towards the identification of genes preferentially expressed in the oviduct during early rat embryo development, we isolated a cDNA fragment (Pr14) by using RNA arbitrarily primed PCR (RAP-PCR), being its expression restricted to oviduct and uterus; its mRNA is mainly expressed in oviduct during late luteal phase and early pregnancy. This fragment is 100% identical to a rat DNA sequence (Accession No. NW_047400) downstream the terminal exon of a Rattus norvegicus gene (Locus Link Accession No. LOC289316) similar to ebaf (endometrial bleeding-associated factor), a novel member of the Transforming Growth Factor superfamily. Northern analyses More >