Previous works have validated that text generation APIs can be stolen through imitation attacks, causing IP violations. In order to protect the IP of text generation APIs, recent work has introduced a watermarking algorithm and utilized the null-hypothesis test as a post-hoc ownership verification on the imitation models. However, we find that it is possible to detect those watermarks via sufficient statistics of the frequencies of candidate watermarking words. To address this drawback, in this paper, we propose a novel Conditional wATERmarking framework (CATER) for protecting the IP of text generation APIs. An optimization method is proposed to decide the watermarking rules that can minimize the distortion of overall word distributions while maximizing the change of conditional word selections. Theoretically, we prove that it is infeasible for even the savviest attacker (they know how CATER works) to reveal the used watermarks from a large pool of potential word pairs based on statistical inspection. Empirically, we observe that high-order conditions lead to an exponential growth of suspicious (unused) watermarks, making our crafted watermarks more stealthy. In addition, CATER can effectively identify IP infringement under architectural mismatch and cross-domain imitation attacks, with negligible impairments on the generation quality of victim APIs. We envision our work as a milestone for stealthily protecting the IP of text generation APIs.

With the growing applications of Deep Learning (DL), especially recent spectacular achievements of Large Language Models (LLMs) such as ChatGPT and LLaMA, the commercial significance of these remarkable models has soared. However, acquiring well-trained models is costly and resource-intensive. It requires a considerable high-quality dataset, substantial investment in dedicated architecture design, expensive computational resources, and efforts to develop technical expertise. Consequently, safeguarding the Intellectual Property (IP) of well-trained models is attracting increasing attention. In contrast to existing surveys overwhelmingly focusing on model IPP mainly, this survey not only encompasses the protection on model level intelligence but also valuable dataset intelligence. Firstly, according to the requirements for effective IPP design, this work systematically summarizes the general and scheme-specific performance evaluation metrics. Secondly, from proactive IP infringement prevention and reactive IP ownership verification perspectives, it comprehensively investigates and analyzes the existing IPP methods for both dataset and model intelligence. Additionally, from the standpoint of training settings, it delves into the unique challenges that distributed settings pose to IPP compared to centralized settings. Furthermore, this work examines various attacks faced by deep IPP techniques. Finally, we outline prospects for promising future directions that may act as a guide for innovative research.

Previous works have validated that text generation APIs can be stolen through imitation attacks, causing IP violations. In order to protect the IP of text generation APIs, recent work has introduced a watermarking algorithm and utilized the null-hypothesis test as a post-hoc ownership verification on the imitation models. However, we find that it is possible to detect those watermarks via sufficient statistics of the frequencies of candidate watermarking words. To address this drawback, in this paper, we propose a novel Conditional wATERmarking framework (CATER) for protecting the IP of text generation APIs. An optimization method is proposed to decide the watermarking rules that can minimize the distortion of overall word distributions while maximizing the change of conditional word selections.

As spiking neural networks (SNNs) gain traction in deploying neuromorphic computing solutions, protecting their intellectual property (IP) has become crucial. Without adequate safeguards, proprietary SNN architectures are at risk of theft, replication, or misuse, which could lead to significant financial losses for the owners. While IP protection techniques have been extensively explored for artificial neural networks (ANNs), their applicability and effectiveness for the unique characteristics of SNNs remain largely unexplored. In this work, we pioneer an investigation into adapting two prominent watermarking approaches, namely, fingerprint-based and backdoor-based mechanisms to secure proprietary SNN architectures. We conduct thorough experiments to evaluate the impact on fidelity, resilience against overwrite threats, and resistance to compression attacks when applying these watermarking techniques to SNNs, drawing comparisons with their ANN counterparts. This study lays the groundwork for developing neuromorphic-aware IP protection strategies tailored to the distinctive dynamics of SNNs.

An artificial intelligence boom is taking over Silicon Valley, with hi-tech firms racing to develop everything from self-driving cars to chatbots capable of writing poetry. Yet AI could also spread conspiracy theories and lies even more quickly than the internet already does – fueling political polarization, hate, violence and mental illness in young people. It could undermine national security with deepfakes. In recent weeks, members of Congress have sounded the alarm over the dangers of AI but no bill has been proposed to protect individuals or stop the development of AI's most threatening aspects. Most lawmakers don't even know what AI is, according to Representative Jay Obernolte, the only member of Congress with a master's degree in artificial intelligence.

We propose a watermarking method for protecting the Intellectual Property (IP) of Generative Adversarial Networks (GANs). The aim is to watermark the GAN model so that any image generated by the GAN contains an invisible watermark (signature), whose presence inside the image can be checked at a later stage for ownership verification. To achieve this goal, a pre-trained CNN watermarking decoding block is inserted at the output of the generator. The generator loss is then modified by including a watermark loss term, to ensure that the prescribed watermark can be extracted from the generated images. The watermark is embedded via fine-tuning, with reduced time complexity. Results show that our method can effectively embed an invisible watermark inside the generated images. Moreover, our method is a general one and can work with different GAN architectures, different tasks, and different resolutions of the output image. We also demonstrate the good robustness performance of the embedded watermark against several post-processing, among them, JPEG compression, noise addition, blurring, and color transformations.

The rights associated with intellectual property are of immense importance to those involved in the development, exploitation and use of computer hardware and software, and information technology generally. Trademarks do not protect technology, but the names or symbols used to distinguish a product in the marketplace. This means that these intellectual property rights accord different types of legal protection on software programmes. The idea must be fixed in definite medium of expression and it must be ascertained that it's the author's own intellectual creation. There are two right or benefits that accrue to a computer programmer with respect to his software programme, which are Economic Right and Moral Right.

The U.S. Department of Commerce's Patent and Trademark Office (USPTO) is asking for the help of experts and the broader public to determine the impact AI will have on intellectual property and "whether new forms of intellectual property protection are needed." A call for public comment was published in the Federal Registrar by the USPTO today in search of answers about such issues as how AI is reshaping perceptions of inventions or whether additional information should be required to claim a deep learning system as an invention since they can have a large number of hidden layers and weights that evolve. To help solicit responses, the notice in the federal registrar comes along with a series of questions such as "what is an AI invention and what does it contain?" "What are the different ways that a natural person can contribute to conception of an AI invention and be eligible to be a named inventor? Structuring data in order to train a model?

The U.S. Department of Commerce's Patent and Trademark Office (USPTO) is asking for the help of experts and the broader public to determine the impact AI will have on intellectual property and "whether new forms of intellectual property protection are needed." A call for public comment was published in the Federal Registrar by the USPTO today in search of answers about such issues as how AI is reshaping perceptions of inventions or whether additional information should be required to claim a deep learning system as an invention since they can have a large number of hidden layers and weights that evolve. To help solicit responses, the notice in the federal registrar comes along with a series of questions such as "what is an AI invention and what does it contain?" "What are the different ways that a natural person can contribute to conception of an AI invention and be eligible to be a named inventor? Structuring data in order to train a model?

A yet-to-be-released Segway Ninebot personal transportation robot is seen onstage during the Intel keynote address at the Consumer Electronics Show in Las Vegas. Editor's Note: Economist Dean Baker is the author of the new book, "Rigged: How Globalization and the Rules of the Modern Economy Were Structured to Make the Rich Richer." There is a recurring concern in discussions about the economy that technology threatens the livelihood of large segments of the workforce. In one version, the robots will take all the jobs, leading to a massive surge in unemployment. A somewhat different version has the development of technology benefiting people with college and advanced degrees to the detriment of those with less education.