Watermark Decoding Technique using Machine Learning for Intellectual Property Protection

The Watermarking is an Intellectual Property (IP) Protection method. It can ensure Field-Programmable Gate Array (FPGA) IPs from encroachment. The IP security of equipment and programming structures is the most significant prerequisite for some FPGA licensed innovation merchants. Advanced watermarking has become a creative innovation for IP assurance as of late. This paper proposes the Publicly Verifiable Watermarking plan for licensed innovation insurance in FPGA structure. The Zero-Knowledge Verification Protocol and Data Matrix strategy are utilized in this watermarking location method. The time stepping is likewise utilized with the zero-information check convention and it can versatility oppose the delicate data spillage and implanting assaults, and is along these lines hearty to the cheating from the prover, verifier, or outsider. The encryption keys are additionally utilized with the information lattice technique and it can restrict the watermark, and make the watermark vigorous against assaults. In this proposed zero-information technique zero rate asset, timing and watermarking overhead can be accomplished. The proposed zero-information watermarking plan causes zero overhead. In this proposed information lattice technique signal-rich-workmanship code picture, can be portrayed. The proposed information network watermarking plan encodes the copyright confirmation data. The zero-information confirmation convention and information grid technique proposed in this paper is executed by MATLAB R2014a in which C programming language is utilized in it and ModelSim 10.5b in which VHDL coding is utilized in it, are running on a PC. The combination instrument Xilinx ISE 14.5 is likewise used to confirm and actualize the watermarking plan

A review and open issues of diverse text watermarking techniques in spatial domain

Nowadays, information hiding is becoming a helpful technique and fetches more attention due to the fast growth of using the internet; it is applied for sending secret information by using different techniques. Watermarking is one of major important technique in information hiding. Watermarking is of hiding secret data into a carrier media to provide the privacy and integrity of information so that no one can recognize and detect it's accepted the sender and receiver. In watermarking, many various carrier formats can be used such as an image, video, audio, and text. The text is most popular used as a carrier files due to its frequency on the internet. There are many techniques variables for the text watermarking; each one has its own robust and susceptible points. In this study, we conducted a review of text watermarking in the spatial domain to explore the term text watermarking by reviewing, collecting, synthesizing and analyze the challenges of different studies which related to this area published from 2013 to 2018. The aims of this paper are to provide an overview of text watermarking and comparison between approved studies as discussed according to the Arabic text characters, payload capacity, Imperceptibility, authentication, and embedding technique to open important research issues in the future work to obtain a robust method

Encryption and Secure Transmission of Telemedicinal Image in Watermarking using DWT HAAR Wavelet Algorithm

This is a result paper .In this paper, watermarking using DWT Haar wavelet algorithm is used.In this papera patient brain image which is to be transmitted using telemedicine is encrypted and the records of patient brain condition is hidden along with patients document and is transmitted along the channel which can not be decrypted by any unauthorized section. The main aim of this paper is to hide the patient information along with the image and to encrypt and transmit the data along with images and to protect it from different kind of attacks and noise that mainly take place in channels. The purpose of using watermarking is that watermarking does not influence the diagnosis to be made by reducing the visual clarity of medical images. Watermarking is implemented here using DWT haar wavelet and the process include complete copyright protection. Experimental result show high imperceptibility where there is no noticeable change in the watermarked image and original image and the patients records is also hidden along with the image which is to be transmitted along the channel that cannot be hacked or attacked by any unauthorized section. The robustness of watermarking scheme is analysed by means of performance evaluation of peak signal to noise ratio (PSNR) DOI: 10.17762/ijritcc2321-8169.150516

Audio, Text, Image, and Video Digital Watermarking Techniques for Security of Media Digital

The proliferation of multimedia content as digital media assets, encompassing audio, text, images, and video, has led to increased risks of unauthorized usage and copyright infringement. Online piracy serves as a prominent example of such misuse. To address these challenges, watermarking techniques have been developed to protect the copyright of digital media while maintaining the integrity of the underlying content. Key characteristics evaluated in watermarking methods include capability, privacy, toughness, and invisibility, with robustness playing a crucial role. This paper presents a comparative analysis of digital watermarking methods, highlighting the superior security and effective watermark image recovery offered by singular value decomposition. The research community has shown significant interest in watermarking, resulting in the development of various methods in both the spatial and transform domains. Transform domain approaches such as Discrete Cosine Transform, Discrete Wavelet Transform, and Singular Value Decomposition, along with their interconnections, have been explored to enhance the effectiveness of digital watermarking methods

QR Code Approach for Examination Process

Using the QR codes is one of the most intriguing ways of digitally connecting consumers to the internet via mobile phones since the mobile phones have become a basic necessity thing of everyone The detection of QR codes, a type of 2D barcode, as described in the literature consists merely in the determination of the boundaries of the symbol region in images obtained with the specific intent of highlighting the symbol .In order to improve the practical application property of the two-dimensional barcode Quick Response (QR) code, we investigate the coding and decoding process of the QR code image. The barcode is a real mechanism for data reads. Data can be stored, embedded and through the scanning device to show. The store of data which being read. In this paper, we present a methodology for creating QR code approach for virtual word examination process by using different techniques like SHA256, encoding, decoding, and Error correction. DOI: 10.17762/ijritcc2321-8169.15024

2006 Annual Report of the Graduate School of Engineering and Management, Air Force Institute of Technology

The Graduate School\u27s Annual Report highlights research focus areas, new academic programs, faculty accomplishments and news, and provides top-level sponsor-funded research data and information

A Property Rights Enforcement and Pricing Model for IIoT Data Marketplaces

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

Deepfakes, Misinformation, and Disinformation in the Era of Frontier AI, Generative AI, and Large AI Models

With the advent of sophisticated artificial intelligence (AI) technologies, the proliferation of deepfakes and the spread of m/disinformation have emerged as formidable threats to the integrity of information ecosystems worldwide. This paper provides an overview of the current literature. Within the frontier AI's crucial application in developing defense mechanisms for detecting deepfakes, we highlight the mechanisms through which generative AI based on large models (LM-based GenAI) craft seemingly convincing yet fabricated contents. We explore the multifaceted implications of LM-based GenAI on society, politics, and individual privacy violations, underscoring the urgent need for robust defense strategies. To address these challenges, in this study, we introduce an integrated framework that combines advanced detection algorithms, cross-platform collaboration, and policy-driven initiatives to mitigate the risks associated with AI-Generated Content (AIGC). By leveraging multi-modal analysis, digital watermarking, and machine learning-based authentication techniques, we propose a defense mechanism adaptable to AI capabilities of ever-evolving nature. Furthermore, the paper advocates for a global consensus on the ethical usage of GenAI and implementing cyber-wellness educational programs to enhance public awareness and resilience against m/disinformation. Our findings suggest that a proactive and collaborative approach involving technological innovation and regulatory oversight is essential for safeguarding netizens while interacting with cyberspace against the insidious effects of deepfakes and GenAI-enabled m/disinformation campaigns.Comment: This paper appears in IEEE International Conference on Computer and Applications (ICCA) 202