Initial Exploration of Cross-Generational Attitudes Towards Piracy of Chinese Online Literature

This chapter seeks to provide initial evidence and provide a baseline for further exploration of Chinese cross-generational audiences' attitude differences towards online literature and digital piracy. Globalization has complicated the many disparate cultural, generational, and national perspectives on intellectual property (IP) protection. IP and IP protection continue to grow in importance in global commerce and international relations. How attitudes towards IP and online content, in particular, evolve generationally is an area of relative under exploration. Data was gathered through an online survey and indicates a trend towards increased awareness and acceptance of IP value and protection. This study provides insight into cross-generational audiences in the important market of China. It may be helpful to those interested in commerce in the areas of online publishing or related industries to help make business decisions in targeting and marketing, to those interested in global commerce and international relations, or those who are researchers in the areas of IP and IP protection.

Convolutional Neural Network Copy Detection with Neural Network Perceptual Hashing

In recent years, many model intellectual property (IP) proof methods for IP protection have been proposed, such as model watermarking and model fingerprinting. However, as an important part of the model IP protection system, the model copy detection task has not received enough attention. With the increasing number of neural network models transmitted and deployed on the Internet, the search for similar models is in great demand, which concurrently triggers the security problem of copy detection of models for IP protection. Due to the high computational complexity, both model watermarking and model fingerprinting lack the capability to efficiently find suspected infringing models among tens of millions of models. In this paper, inspired by the hash-based image retrieval methods, we propose a perceptual hashing algorithm for convolutional neural networks (CNNs). The proposed perceptual hashing algorithm can convert the weights of CNNs to fixed-length binary hash codes so that the lightly modified version has the similar hash code as the original model. By comparing the similarity of a pair of hash codes between a query model and a test model in the model library, the similar versions of a query model can be retrieved efficiently. To the best of our knowledge, this is the first perceptual hashing algorithm for CNNs. The experiment performed on a model library containing 3,565 models indicates that our proposed perceptual hashing scheme has a superior copy detection performance.

Convolutional Neural Network Copy Detection with Neural Network Perceptual Hashing

In recent years, many model intellectual property (IP) proof methods for IP protection have been proposed, such as model watermarking and model fingerprinting. However, as an important part of the model IP protection system, the model copy detection task has not received enough attention. With the increasing number of neural network models transmitted and deployed on the Internet, the search for similar models is in great demand, which concurrently triggers the security problem of copy detection of models for IP protection. Due to the high computational complexity, both model watermarking and model fingerprinting lack the capability to efficiently find suspected infringing models among tens of millions of models. In this paper, inspired by the hash-based image retrieval methods, we propose a perceptual hashing algorithm for convolutional neural networks (CNNs). The proposed perceptual hashing algorithm can convert the weights of CNNs to fixed-length binary hash codes so that the lightly modified version has the similar hash code as the original model. By comparing the similarity of a pair of hash codes between a query model and a test model in the model library, the similar versions of a query model can be retrieved efficiently. To the best of our knowledge, this is the first perceptual hashing algorithm for CNNs. The experiment performed on a model library containing 3,565 models indicates that our proposed perceptual hashing scheme has a superior copy detection performance.

Spillover Effects of Intellectual Property Protection in the Interwar Aircraft Industry

Can strengthening intellectual property (IP) protection for producers of one good affect innovation in other related goods? To answer this question, we exploit a unique policy experiment in the interwar military aircraft industry. Airframe designs had little IP protection before 1926, but changes passed by Congress in 1926 provided airframe manufacturers with enhanced property rights over new designs. We show that granting property rights to airframe producers increased innovation in airframes, but slowed innovation in aero-engines, a complementary good where there was no change in the availability of IP protection. We propose and test a simple theory that explains these patterns.

What’s yours is ours: waiving intellectual property protections for COVID-19 vaccines

This paper gives an ethical argument for temporarily waiving intellectual property (IP) protections for COVID-19 vaccines. It examines two proposals under discussion at the World Trade Organization (WTO): the India/South Africa proposal and the WTO Director General proposal. Section I explains the background leading up to the WTO debate. Section II rebuts ethical arguments for retaining current IP protections, which appeal to benefiting society by spurring innovation and protecting rightful ownership. It sets forth positive ethical arguments for a temporary waiver that appeal to standing in solidarity and holding companies accountable. After examining built-in exceptions to existing agreements and finding them inadequate, the paper replies to objections to a temporary waiver and concludes, in section III, that the ethical argument for temporarily waiving IP protection for COVID-19 vaccines is strong.