334 research outputs found

    A Blind Multiple Watermarks based on Human Visual Characteristics

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    Digital watermarking is an alternative solution to prevent unauthorized duplication, distribution and breach of ownership right. This paper proposes a watermarking scheme for multiple watermarks embedding. The embedding of multiple watermarks use a block-based scheme based on human visual characteristics. A threshold is used to determine the watermark values by modifying first column of the orthogonal U matrix obtained from Singular Value Decomposition (SVD). The tradeoff between normalize cross-correlation and imperceptibility of watermarked image from quantization steps was used to achieve an optimal threshold value. The results show that our proposed multiple watermarks scheme exhibit robustness against signal processing attacks. The proposed scheme demonstrates that the watermark recovery from chrominance blue was resistant against different types of attacks

    Dual Defense: Adversarial, Traceable, and Invisible Robust Watermarking against Face Swapping

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    The malicious applications of deep forgery, represented by face swapping, have introduced security threats such as misinformation dissemination and identity fraud. While some research has proposed the use of robust watermarking methods to trace the copyright of facial images for post-event traceability, these methods cannot effectively prevent the generation of forgeries at the source and curb their dissemination. To address this problem, we propose a novel comprehensive active defense mechanism that combines traceability and adversariality, called Dual Defense. Dual Defense invisibly embeds a single robust watermark within the target face to actively respond to sudden cases of malicious face swapping. It disrupts the output of the face swapping model while maintaining the integrity of watermark information throughout the entire dissemination process. This allows for watermark extraction at any stage of image tracking for traceability. Specifically, we introduce a watermark embedding network based on original-domain feature impersonation attack. This network learns robust adversarial features of target facial images and embeds watermarks, seeking a well-balanced trade-off between watermark invisibility, adversariality, and traceability through perceptual adversarial encoding strategies. Extensive experiments demonstrate that Dual Defense achieves optimal overall defense success rates and exhibits promising universality in anti-face swapping tasks and dataset generalization ability. It maintains impressive adversariality and traceability in both original and robust settings, surpassing current forgery defense methods that possess only one of these capabilities, including CMUA-Watermark, Anti-Forgery, FakeTagger, or PGD methods

    A Review of Copyright Protection Approaches in Electronic Commerce (Watermarking Method)

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    Digital watermarking is the best way to protect intellectual property from illicit copying. Digital watermarks hide the identity of an image or audio file in its noise signal. A pattern of bits inserted into a digital image, audio or video file that identifies the files copyright information. The purpose of this paper is to provide copyright protection for intellectual property that\u27s in digital format. In this career we review digital watermarks an application of steganography

    Deep Intellectual Property: A Survey

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    With the widespread application in industrial manufacturing and commercial services, well-trained deep neural networks (DNNs) are becoming increasingly valuable and crucial assets due to the tremendous training cost and excellent generalization performance. These trained models can be utilized by users without much expert knowledge benefiting from the emerging ''Machine Learning as a Service'' (MLaaS) paradigm. However, this paradigm also exposes the expensive models to various potential threats like model stealing and abuse. As an urgent requirement to defend against these threats, Deep Intellectual Property (DeepIP), to protect private training data, painstakingly-tuned hyperparameters, or costly learned model weights, has been the consensus of both industry and academia. To this end, numerous approaches have been proposed to achieve this goal in recent years, especially to prevent or discover model stealing and unauthorized redistribution. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field. More than 190 research contributions are included in this survey, covering many aspects of Deep IP Protection: challenges/threats, invasive solutions (watermarking), non-invasive solutions (fingerprinting), evaluation metrics, and performance. We finish the survey by identifying promising directions for future research.Comment: 38 pages, 12 figure

    High capacity data embedding schemes for digital media

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    High capacity image data hiding methods and robust high capacity digital audio watermarking algorithms are studied in this thesis. The main results of this work are the development of novel algorithms with state-of-the-art performance, high capacity and transparency for image data hiding and robustness, high capacity and low distortion for audio watermarking.En esta tesis se estudian y proponen diversos métodos de data hiding de imágenes y watermarking de audio de alta capacidad. Los principales resultados de este trabajo consisten en la publicación de varios algoritmos novedosos con rendimiento a la altura de los mejores métodos del estado del arte, alta capacidad y transparencia, en el caso de data hiding de imágenes, y robustez, alta capacidad y baja distorsión para el watermarking de audio.En aquesta tesi s'estudien i es proposen diversos mètodes de data hiding d'imatges i watermarking d'àudio d'alta capacitat. Els resultats principals d'aquest treball consisteixen en la publicació de diversos algorismes nous amb rendiment a l'alçada dels millors mètodes de l'estat de l'art, alta capacitat i transparència, en el cas de data hiding d'imatges, i robustesa, alta capacitat i baixa distorsió per al watermarking d'àudio.Societat de la informació i el coneixemen

    Digital watermarking and novel security devices

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    EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Improved content based watermarking for images

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    Due to improvements in imaging technologies and the ease with which digital content can be created and manipulated, there is need for the copyright protection of digital content. It is also essential to have techniques for authentication of the content as well as the owner. To this end, this thesis proposes a robust and transparent scheme of watermarking that exploits the human visual systems’ sensitivity to frequency, along with local image characteristics obtained from the spatial domain, improving upon the content based image watermarking scheme of Kay and Izquierdo. We implement changes in this algorithm without much distortion to the image, while making it possible to extract the watermark by use of correlation. The underlying idea is generating a visual mask based on the human visual systems’ perception of image content. This mask is used to embed a decimal sequence, while keeping its amplitude below the distortion sensitivity of the image pixel. We consider texture, luminance, corner and the edge information in the image to generate a mask that makes the addition of the watermark less perceptible to the human eye. The operation of embedding and extraction of the watermark is done in the frequency domain thereby providing robustness against common frequency-based attacks including image compression and filtering. We use decimal sequences for watermarking instead of pseudo random sequences, providing us with a greater flexibility in the choice of sequence. Weighted Peak Signal to Noise Ratio is used to evaluate the perceptual change between the original and the watermarked image

    A Property Rights Enforcement and Pricing Model for IIoT Data Marketplaces

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    학위논문(석사)--서울대학교 대학원 :공과대학 협동과정 기술경영·경제·정책전공,2019. 8. Jörn Altmann.The Industrial Internet of Things (IIoT) has become a valuable data source for products and services based on advanced data analytics. However, evidence suggests that industries are suffering a significant loss of value creation from insufficient IIoT data sharing. We argue that the limited utilization of the Sensing as a Service business model is caused by the economic and technological characteristics of sensor data, and the corresponding absence of applicable digital rights management models. Therefore, we propose a combined property rights enforcement and pricing model to solve the IIoT data sharing incentive problem.산업용 사물 인터넷 (IIoT) 데이터가 제품과 서비스를 위한 중요한 고급 데이터 소스로 여겨지고 있지만, 여전히 수 많은 기업들은 불충분한 산업용 사물 인터넷 데이터 공유 시스템으로 인하여 고충을 겪고 있다. 방대한 분량의 산업용 데이터가 제대로 거래되지 못하고 있으며, 이는 데이터의 커다란 가치 손실로 이어지고 있다. 본 연구에서는 서비스로서의 센싱 (Sensing as a Service) 비지니스 모델이 한정적으로 적용되고 있는 원인이 해당 정보의 경제적, 기술적 특징들을 반영하는 디지털 권리 시스템의 부재에 기인한다고 보고 있다. 따라서 본 연구에서는 산업용 사물 인터넷 데이터에 대한 지적재산권 집행 시스템과 데이터 가격산정 모델을 제안하여 산업용 사물 인터넷 데이터 공유 인센티브 문제를 해결하고자 한다.1 Introduction 1 1.1 Background 1 1.2 Problem Description 6 1.3 Research Objective and Question 8 1.4 Methodology 8 1.5 Contributions 9 1.6 Structure 10 2 Literature Review 11 2.1 Sensing as a Service 11 2.2 Economic Characteristics of IIoT Data 14 2.2.1 Property Rights of Data 18 2.2.2 Licensing of IIoT Data 23 2.3 IIoT Data Marketplaces 25 2.3.1 Use-cases and Value Propositions 30 2.3.2 Market Structures and Pricing Models 34 2.4 Digital Rights Management for IIoT 36 3 Model 44 3.1 Assumptions 45 3.2 Watermarking Technique 47 3.2.1 Function 48 3.2.2 Example 50 3.2.3 Robustness 51 3.3 Economic Reasoning 54 3.3.1 The Quality Gap 55 3.3.2 Cost of Watermarking (CoW) 57 3.3.3 Cost of Attacking (CoA) 58 4 Analytical Analysis 60 4.1 Equilibrium Between CoW and CoA 60 4.2 Determining the Optimal Quality Gap 62 4.3 Applicability of the Quality Gap Function 64 5 Conclusion 66 5.1 Summary 66 5.2 Discussion 66 6 Limitations and Future Research 68 References 70 Abstract (Korean) 79Maste
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