Intellectual Property Protection Methods Research Articles

A spatiotemporal chaos based deep learning model watermarking scheme

With deep learning techniques achieving great results in modern industry, the intellectual property (IP) protection for deep learning models has attracted the attention of academics and engineers. However, training a commercially viable deep learning model usually needs professional resources and time. Once a malicious user clones, illegally distributes and uses the model, it can infringe on the model owner's IP and even steal its market share. Among the existing IP protection methods, scholars prefer the black-box watermarking approaches, of which the content of the trigger set and the label are the key part of the watermarking technique. However, most schemes do not consider the security and invisibility of the trigger set, which allows attackers to easily trigger the model by creating a fake trigger set, thereby committing a fraudulent ownership claim attack and claiming the ownership belongs to themselves. To overcome these drawbacks, we proposed a spatiotemporal chaotic data annotation method. Firstly, the unpredictability and acyclicity of chaos make the model resistant to fraudulent ownership claim attacks, statistical inference and other common machine learning attacks; Secondly, the trigger set and parameters are independent of each other, guaranteeing the security of the key; Thirdly, the spatiotemporal chaotic system provides a large key space, which meets the commercialization needs of deep learning models. Theoretical analysis and experimental results show that our scheme has security, practicality and robustness. To further validate the superiority of the proposed method, we also compare it with the Logistic chaotic annotation watermarking-based method, and the results show that our method performs better in terms of robustness, effectiveness, completeness, fidelity, security and practicality.

EncryIP: A Practical Encryption-Based Framework for Model Intellectual Property Protection

In the rapidly growing digital economy, protecting intellectual property (IP) associated with digital products has become increasingly important. Within this context, machine learning (ML) models, being highly valuable digital assets, have gained significant attention for IP protection. This paper introduces a practical encryption-based framework called EncryIP, which seamlessly integrates a public-key encryption scheme into the model learning process. This approach enables the protected model to generate randomized and confused labels, ensuring that only individuals with accurate secret keys, signifying authorized users, can decrypt and reveal authentic labels. Importantly, the proposed framework not only facilitates the protected model to multiple authorized users without requiring repetitive training of the original ML model with IP protection methods but also maintains the model's performance without compromising its accuracy. Compared to existing methods like watermark-based, trigger-based, and passport-based approaches, EncryIP demonstrates superior effectiveness in both training protected models and efficiently detecting the unauthorized spread of ML models.

An Explainable Intellectual Property Protection Method for Deep Neural Networks based on Intrinsic Features

An Explainable Intellectual Property Protection Method for Deep Neural Networks based on Intrinsic Features

Mitigating Query-based Neural Network Fingerprinting via Data Augmentation

Protecting the intellectual property (IP) of deep neural network (DNN) models becomes essential and urgent with the rapidly increasing cost of DNN training. Fingerprinting is one promising IP protection method that queries suspicious models with specific fingerprint samples to infer and verify IP by comparing the predictions with pre-defined labels. Based on utilizing unique features of target models, various DNN fingerprinting methods are proposed to effectively verify the IP of models remotely with a meager false-positive ratio. In this paper, we propose a novel attack to mitigate query-based fingerprinting methods based on data augmentation methods. We propose a randomized transformation on input samples to significantly mislead the fingerprint samples’ prediction and compromise the IP verification. Then, our attack can keep the model utility with an acceptable accuracy drop in the data-free scenario (i.e. without any samples) or achieve much higher precision in the data-limited scenario (i.e. with a small number of samples with the same distribution). An intensive evaluation of three well-known model structures and three well-known datasets shows that our attack can effectively mitigate five query-based DNN fingerprinting methods in top-tier conferences.

Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluations

The training and creation of deep learning model is usually costly, thus it can be regarded as an intellectual property (IP) of the model creator. However, malicious users who obtain high-performance models may illegally copy, redistribute, or abuse the models without permission. To deal with such security threats, a few deep neural networks (DNN) IP protection methods have been proposed in recent years. This paper attempts to provide a review of the existing DNN IP protection works and also an outlook. First, we propose the first taxonomy for DNN IP protection methods in terms of six attributes: scenario, mechanism, capacity, type, function, and target models. Then, we present a survey on existing DNN IP protection works in terms of the above six attributes, especially focusing on the challenges these methods face, whether these methods can provide proactive protection, and their resistances to different levels of attacks. After that, we analyze the potential attacks on DNN IP protection methods from the aspects of model modifications, evasion attacks, and active attacks. Besides, a systematic evaluation method for DNN IP protection methods with respect to basic functional metrics, attack-resistance metrics, and customized metrics for different application scenarios is given. Lastly, future research opportunities and challenges on DNN IP protection are presented.

Perceived importance of intellectual property protection methods by Korean SMEs involved in product innovation and their value appropriation

ABSTRACT We investigate the sectoral patterns of relationships between SMEs’ perception of the importance of intellectual property (IP) protection methods and their value appropriation from innovation in countries with weak IP rights regime. By using a Heckman selection model to analyze 1,020 Korean SMEs involved in product innovation, we find that formal IP rights have a lower contribution to appropriation than the expected level. Instead, we find some positive links between SMEs’ affirmative perception of informal means of IP protection, particularly complex design, and their appropriation in both complex and discrete sectors. We discuss IP management and policy implications for enhancing the SMEs’ value appropriation.

Review on blockchain technology and its application to the simple analysis of intellectual property protection

Blockchain is a widely used decentralised infrastructure. Blockchain technology has the decentration of network, the unforgeability of block data, etc. Therefore, blockchain technology has developed rapidly in recent years, and many organisations are involved. Applications are generally optimistic. This paper systematically introduces the background development status of blockchain, and analyses the operation mechanism, characteristics and possible application scenarios of blockchain technology from a technical perspective. Finally, the blockchain technology is applied to the intellectual property protection method as an example to study domestic and foreign examples and analyse existing problems. The review article aims to provide assistance for the application of blockchain technology.

Review on blockchain technology and its application to the simple analysis of intellectual property protection

Blockchain is a widely used decentralised infrastructure. Blockchain technology has the decentration of network, the unforgeability of block data, etc. Therefore, blockchain technology has developed rapidly in recent years, and many organisations are involved. Applications are generally optimistic. This paper systematically introduces the background development status of blockchain, and analyses the operation mechanism, characteristics and possible application scenarios of blockchain technology from a technical perspective. Finally, the blockchain technology is applied to the intellectual property protection method as an example to study domestic and foreign examples and analyse existing problems. The review article aims to provide assistance for the application of blockchain technology.

THE INVESTIGATION OF INDUSTRIAL INTELLECTUAL PROPERTY PROTECTION METHODS AND THEIR IMPROVEMENT / PRAMONINĖS INTELEKTINĖS NUOSAVYBĖS APSAUGOS METODŲ TYRIMAS IR JŲ TOBULINIMAS

This article main purpose is to analyse the existing industrial intellectual property protection methods and to provide some suggestions for improvements. It gives insights on all possible protection methods, when it is best to use it and how to choose. This article shows a different approach taken to solve a complex and lengthy process. In this paper, Business Process Modelling techniques have been introduced and applied providing a different point of view to solve some of the issues. The main idea is to remove non-value adding processes and introduce ICT based software between different parties involved.

The Moderating Effect of Intellectual Property Rights on Relationship between Innovation and Company Performance in Manufacturing Sector

Abstract This paper examines whether the intellectual property rights of manufacturing company moderate the relationships between innovation and company performance in manufacturing sector. We argue that technologically more advanced manufacturers rely on exploiting intellectual capital in their business. Thus, there is a growth of the use of formal intellectual property protection methods because protective rights are critical as it prevents replication by potential competitors. The objective of the study is to understand how intellectual property rights contribute as new ventures transform their innovation potential and creativity into market value, profit, productivity, new jobs, wages, and competitiveness. The multi-item constructs were tested by exploratory factor analysis (EFA) each set of scale items using the principal axis with the varimax method provided in SPSS. This procedure may help to reduce multi-collinearity or error variance correlations among indicators in the confirmatory factor analysis (CFA). The findings indicate a positive correlation between patenting and new ventures’ growth, access to venture capital and new knowledge. This study provides empirical evidence that intellectual property is a set of valuable intangible assets and managing innovation better than its competitors is one of the main objectives of a manufacturing company.

Studies on The Design Intellectual Property Protection Method in Digital Fabrication environment - Mainly with Analysis on 3D printing industry and Understanding of Intellectual Property Rights -

Studies on The Design Intellectual Property Protection Method in Digital Fabrication environment - Mainly with Analysis on 3D printing industry and Understanding of Intellectual Property Rights -

"Strategy, organization and IP management in knowledge intensive service firms"

This paper studies how knowledge intensive business service (KIBS) firms assess and chose among different appropriability mechanisms - including formal, contractual and strategic intellectual property protection methods - to capture value from innovation. Drawing on strategy and organization theories, we propose that a firm’s competitive strategy and organizational approach to knowledge co-determine (together with other innovation-, firm- and industry-level factors) the effectiveness of different value appropriability mechanisms. The results from multivariate probit regression analysis using data from an original survey and 255 service product and process innovations that were introduced by 167 British and American publicly-traded KIBS firms, confirm our basic proposition. We thus contribute to the study of the determinants of IP management and improve our understanding of managerial practices for capturing value from service innovation.

Profiting from business model innovation: Evidence from Pay-As-You-Drive auto insurance

The emergent business model literature, revolving mainly around the mechanisms through which new business models create and deliver value, has left the value capture challenge under-explored. This paper examines how an incumbent firm profits from business model innovation through the study of Pay-As-You-Drive auto insurance. Although business models do not warrant formal intellectual property (IP) protection, their constituent components (e.g. business methods and brands) often do. Drawing on the profiting-from-innovation framework, we find that formal and strategic IP protection methods play complementary roles. Initially, formal IP rights are used primarily as a defensive strategy, as vehicles for packaging and trading know-how, and most importantly as a means of “buying time” to build specialised complementary assets. Long-term competitiveness, however, depends on whether the innovator builds a strong position in specialised complementary assets and is capable of reconfiguring them over time in line with changes in the market environment. Thus, we explicate the complex mechanism and dynamic capability for capturing value from business model innovation.

Intellectual Property Protection Strategies of Foreign Companies in Korea

This paper aims at analyzing the intellectual property (IP) protection strategies of foreign companies in Korea to suggest policy implications. To this end, the Korean Innovation Survey (KIS) data conducted by Science and Technology Policy Institute (STEPI) is utilized to analyze various IP protection methods ? secrecy, lead time and complex design, as well as patent. Specifically, the following three questions were in focus: What are the methods used by foreign companies to protect their IP? How effective are the methods? Can they make more diversified IP protection strategies, compared with domestic companies? Major empirical findings are as follows. Compared with domestic companies, foreign companies are more likely to use secrecy and lead time (rather than patent) and evaluate their effectiveness higher. Also, they tend to utilize more profiles of IP protection strategies than domestic companies. However, the results are true only in the service sector, where competitiveness is relatively low, but not in the manufacturing sector. This paper emphasizes the importance of spillovers from FDI or foreign R&D to Korean companies, especially in the service sector, and suggests policy implications for maximizing spillovers - the facilitation of academy-industry knowledge transfer channels, the improvement of IP protection environments and so on.

Interdependence and spillovers: is firm performance affected by others’ innovation activities?

The creation of new knowledge is a case in which agents' behaviour can affect the performance of other actors positively, given that new knowledge creates positive externalities in the market. In this context, we investigate the existence of performance spillovers associated with innovation activities by quantifying the innovation produced in surrounding firms and controlling for the fact that a firm is itself an innovation producer. We use data from the Third Community Innovation Survey (CIS III) that measures innovation in a broad way, not reducing it to R&D and patents, which departs from previous literature on spillovers. Furthermore, to tackle the endogeneity of the innovation variables on the firm production decision, we resort to the firm intellectual property protection methods as an instrument. We found a positive spillover of innovation on firm value added. The results also show that process innovation spillovers are more prevalent than product innovation spillovers.

Complementarities between strategies to protect inventions and innovations: evidence from manufacturing SMEs

How should innovative firms manage the protection of their inventions and innovations from imitation when having to choose between various intellectual property protection methods? Data on manufacturing SMEs are used to investigate complementarities and independence between six protection mechanisms in order to see how they mix them to protect their technological innovations. The results of the study show that SMEs devise four generic protection strategies that are based on complementary mixes of mechanisms in order to protect their inventions and innovations. Moreover, the results also show that there are many important differences in the determinants of the different protection methods.