381 research outputs found

    Oscar Wilde's Orientalism and Late Nineteenth-Century European Consumer Culture

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    Adaptive digital watermarking scheme based on support vector machines and optimized genetic algorithm

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    Digital watermarking is an effective solution to the problem of copyright protection, thus maintaining the security of digital products in the network. An improved scheme to increase the robustness of embedded information on the basis of discrete cosine transform (DCT) domain is proposed in this study. The embedding process consisted of two main procedures. Firstly, the embedding intensity with support vector machines (SVMs) was adaptively strengthened by training 1600 image blocks which are of different texture and luminance. Secondly, the embedding position with the optimized genetic algorithm (GA) was selected. To optimize GA, the best individual in the first place of each generation directly went into the next generation, and the best individual in the second position participated in the crossover and the mutation process. The transparency reaches 40.5 when GA’s generation number is 200. A case study was conducted on a 256 × 256 standard Lena image with the proposed method. After various attacks (such as cropping, JPEG compression, Gaussian low-pass filtering (3, 0. 5), histogram equalization, and contrast increasing (0.5, 0.6)) on the watermarked image, the extracted watermark was compared with the original one. Results demonstrate that the watermark can be effectively recovered after these attacks. Even though the algorithm is weak against rotation attacks, it provides high quality in imperceptibility and robustness and hence it is a successful candidate for implementing novel image watermarking scheme meeting real timelines

    Security and privacy for data mining of RFID-enabled product supply chains

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    The e-Pedigree used for verifying the authenticity of the products in RFID-enabled product supply chains plays a very important role in product anti-counterfeiting and risk management, but it is also vulnerable to malicious attacks and privacy leakage. While the radio frequency identification (RFID) technology bears merits such as automatic wireless identification without direct eye-sight contact, its security has been one of the main concerns in recent researches such as tag data tampering and cloning. Moreover, privacy leakage of the partners along the supply chains may lead to complete compromise of the whole system, and in consequence all authenticated products may be replaced by the faked ones! Quite different from other conventional databases, datasets in supply chain scenarios are temporally correlated, and every party of the system can only be semi-trusted. In this paper, a system that incorporates merits of both the secure multi-party computing and differential privacy is proposed to address the security and privacy issues, focusing on the vulnerability analysis of the data mining with distributed EPCIS datasets of e-pedigree having temporal relations from multiple range and aggregate queries in typical supply chain scenarios and the related algorithms. Theoretical analysis shows that our proposed system meets perfectly our preset design goals, while some of the other problems leave for future research
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