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The Chicago-Kent, Illinois Tech IP Program cordially invites you to: "What’s the Skinny? Drug Labels and Patent Infringement," Tuesday, October 28, 2025 at 12-1 pm.
The panel is open to the public but please RSVP in advance here: https://

"we affirm that no country should conduct or
support ICT-enabled theft of intellectual property, including trade secrets or other confidential
business information, with the intent of providing competitive advantages to companies or
commercial sectors." (G20-Communiqué 2015)
Passt nicht nur zu den KI-Lumpen - auch seit 2015 wissen wir, dass kommerzielle Infrastrukturdienste für die Wissenschaft vollgepfropft sind mit Tracking-Tools, die Informationen abschöpfen…

Elise Explains IPcast
Provides simple, expert guidance on trade marks, design registrations, copyright, brand strategy, and intellectual property law in Australia...
Great Australian Pods Podcast Directory: https://www.greataustralianpods.com/elise-explains-ipcast/

On 30 April, 1993, CERN published the complete source code to the #WWW protocols developed by @…, as well as his client and server software with this statement: “CERN relinquishes all intellectual property rights to this code, both source and binary and permission is given to anyon…

A lot of the social fallout and collateral damage caused by #AI is actually rooted in unregulated platform capitalism and neoliberal ad-consumer-waste-cycles: social media as mass manipulation, intellectual property laws that haven't been updated to the internet at all, gig economy exploitation... but most importantly:
Tech monopolies that are allowed to program dark pattern mechanisms int…

OBsmith: Testing JavaScript Obfuscator using LLM-powered sketching
Shan Jiang, Chenguang Zhu, Sarfraz Khurshid
https://arxiv.org/abs/2510.10066 https://arx…

OBsmith: Testing JavaScript Obfuscator using LLM-powered sketching
JavaScript obfuscators are widely deployed to protect intellectual property and resist reverse engineering, yet their correctness has been largely overlooked compared to performance and resilience. Existing evaluations typically measure resistance to deobfuscation, leaving the critical question of whether obfuscators preserve program semantics unanswered. Incorrect transformations can silently alter functionality, compromise reliability, and erode security-undermining the very purpose of obfusc…

AWM: Accurate Weight-Matrix Fingerprint for Large Language Models
Boyi Zeng, Lin Chen, Ziwei He, Xinbing Wang, Zhouhan Lin
https://arxiv.org/abs/2510.06738 https://

AWM: Accurate Weight-Matrix Fingerprint for Large Language Models
Protecting the intellectual property of large language models (LLMs) is crucial, given the substantial resources required for their training. Consequently, there is an urgent need for both model owners and third parties to determine whether a suspect LLM is trained from scratch or derived from an existing base model. However, the intensive post-training processes that models typically undergo-such as supervised fine-tuning, extensive continued pretraining, reinforcement learning, multi-modal ex…

Reading Between the Lines: Towards Reliable Black-box LLM Fingerprinting via Zeroth-order Gradient Estimation
Shuo Shao, Yiming Li, Hongwei Yao, Yifei Chen, Yuchen Yang, Zhan Qin
https://arxiv.org/abs/2510.06605

Reading Between the Lines: Towards Reliable Black-box LLM Fingerprinting via Zeroth-order Gradient Estimation
The substantial investment required to develop Large Language Models (LLMs) makes them valuable intellectual property, raising significant concerns about copyright protection. LLM fingerprinting has emerged as a key technique to address this, which aims to verify a model's origin by extracting an intrinsic, unique signature (a "fingerprint") and comparing it to that of a source model to identify illicit copies. However, existing black-box fingerprinting methods often fail to generate distinctiv…

ActiveMark: on watermarking of visual foundation models via massive activations
Anna Chistyakova, Mikhail Pautov
https://arxiv.org/abs/2510.04966 https://a…

ActiveMark: on watermarking of visual foundation models via massive activations
Being trained on large and vast datasets, visual foundation models (VFMs) can be fine-tuned for diverse downstream tasks, achieving remarkable performance and efficiency in various computer vision applications. The high computation cost of data collection and training motivates the owners of some VFMs to distribute them alongside the license to protect their intellectual property rights. However, a dishonest user of the protected model's copy may illegally redistribute it, for example, to make …

"Creative Commons is a license that lets the creators of intellectual property define how that property may be used by others. Partially this license is a reaction to the poor way #copyright law is being viewed today...
...gives creators a way to provide for more interaction with their ideas. And this interaction is a great way to market, in the right circumstances. More managers shou…

DeepV: A Model-Agnostic Retrieval-Augmented Framework for Verilog Code Generation with a High-Quality Knowledge Base
Zahin Ibnat, Paul E. Calzada, Rasin Mohammed Ihtemam, Sujan Kumar Saha, Jingbo Zhou, Farimah Farahmandi, Mark Tehranipoor
https://arxiv.org/abs/2510.05327

DeepV: A Model-Agnostic Retrieval-Augmented Framework for Verilog Code Generation with a High-Quality Knowledge Base
As large language models (LLMs) continue to be integrated into modern technology, there has been an increased push towards code generation applications, which also naturally extends to hardware design automation. LLM-based solutions for register transfer level (RTL) code generation for intellectual property (IP) designs have grown, especially with fine-tuned LLMs, prompt engineering, and agentic approaches becoming popular in literature. However, a gap has been exposed in these techniques, as t…

Towards Human-Centered RegTech: Unpacking Professionals' Strategies and Needs for Using LLMs Safely
Siying Hu, Yaxing Yao, Zhicong Lu
https://arxiv.org/abs/2510.01638 https:…

Towards Human-Centered RegTech: Unpacking Professionals' Strategies and Needs for Using LLMs Safely
Large Language Models are profoundly changing work patterns in high-risk professional domains, yet their application also introduces severe and underexplored compliance risks. To investigate this issue, we conducted semi-structured interviews with 24 highly-skilled knowledge workers from industries such as law, healthcare, and finance. The study found that these experts are commonly concerned about sensitive information leakage, intellectual property infringement, and uncertainty regarding the …

Legal Matters in Research Software: A Few Things Worth Discussing
Giuditta Parolini
https://arxiv.org/abs/2509.24646 https://arxiv.org/pdf/2509.24646

Legal Matters in Research Software: A Few Things Worth Discussing
The paper discusses legal aspects relevant to the development of research software and practical approaches taken by research software engineers to deal with them. Intellectual Property Rights on software are considered alongside licensing choices made by the research community. The discussion addresses the ambiguities in the identification of the copyright holder of research software, the uncertainty surrounding liability, and remarks the varying level of support on legal matters provided by r…

Latent Diffusion Unlearning: Protecting Against Unauthorized Personalization Through Trajectory Shifted Perturbations
Naresh Kumar Devulapally, Shruti Agarwal, Tejas Gokhale, Vishnu Suresh Lokhande
https://arxiv.org/abs/2510.03089

Latent Diffusion Unlearning: Protecting Against Unauthorized Personalization Through Trajectory Shifted Perturbations
Text-to-image diffusion models have demonstrated remarkable effectiveness in rapid and high-fidelity personalization, even when provided with only a few user images. However, the effectiveness of personalization techniques has lead to concerns regarding data privacy, intellectual property protection, and unauthorized usage. To mitigate such unauthorized usage and model replication, the idea of generating ``unlearnable'' training samples utilizing image poisoning techniques has emerged. Existing…

Modeling and Managing Temporal Obligations in GUCON Using SPARQL-star and RDF-star
Ines Akaichi, Giorgos Flouris, Irini Fundulaki, Sabrina Kirrane
https://arxiv.org/abs/2510.04652

Modeling and Managing Temporal Obligations in GUCON Using SPARQL-star and RDF-star
In the digital age, data frequently crosses organizational and jurisdictional boundaries, making effective governance essential. Usage control policies have emerged as a key paradigm for regulating data usage, safeguarding privacy, protecting intellectual property, and ensuring compliance with regulations. A central mechanism for usage control is the handling of obligations, which arise as a side effect of using and sharing data. Effective monitoring of obligations requires capturing usage trac…

JSProtect: A Scalable Obfuscation Framework for Mini-Games in WeChat
Zhihao Li, Chaozheng Wang, Zongjie Li, Xinyong Peng, Zelin Su, Qun Xia, Haochuan Lu, Ting Xiong, Man Ho Lam, Shuzheng Gao, Yuchong Xie, Cuiyun Gao, Shuai Wang, Yuetang Deng, Huafeng Ma
https://arxiv.org/abs/2509.24498

JSProtect: A Scalable Obfuscation Framework for Mini-Games in WeChat
The WeChat mini-game ecosystem faces rampant intellectual property theft to other platforms via secondary development, yet existing JavaScript obfuscation tools are ill-equipped for large-scale applications, suffering from prohibitive processing times, severe runtime performance degradation, and unsustainable code size inflation. This paper introduces JSProtect, a high-throughput parallelized obfuscation framework designed to overcome these fundamental limitations. At the core of our framework …

Secure and Robust Watermarking for AI-generated Images: A Comprehensive Survey
Jie Cao, Qi Li, Zelin Zhang, Jianbing Ni
https://arxiv.org/abs/2510.02384 https://

Secure and Robust Watermarking for AI-generated Images: A Comprehensive Survey
The rapid advancement of generative artificial intelligence (Gen-AI) has facilitated the effortless creation of high-quality images, while simultaneously raising critical concerns regarding intellectual property protection, authenticity, and accountability. Watermarking has emerged as a promising solution to these challenges by distinguishing AI-generated images from natural content, ensuring provenance, and fostering trustworthy digital ecosystems. This paper presents a comprehensive survey of…

Stealing AI Model Weights Through Covert Communication Channels
Valentin Barbaza, Alan Rodrigo Diaz-Rizo, Hassan Aboushady, Spyridon Raptis, Haralampos-G. Stratigopoulos
https://arxiv.org/abs/2510.00151

Stealing AI Model Weights Through Covert Communication Channels
AI models are often regarded as valuable intellectual property due to the high cost of their development, the competitive advantage they provide, and the proprietary techniques involved in their creation. As a result, AI model stealing attacks pose a serious concern for AI model providers. In this work, we present a novel attack targeting wireless devices equipped with AI hardware accelerators. The attack unfolds in two phases. In the first phase, the victim's device is compromised with a hardw…

Logic Solver Guided Directed Fuzzing for Hardware Designs
Raghul Saravanan, Sai Manoj P D
https://arxiv.org/abs/2509.26509 https://arxiv.org/pdf/2509.26509…

Logic Solver Guided Directed Fuzzing for Hardware Designs
The ever-increasing complexity of design specifications for processors and intellectual property (IP) presents a formidable challenge for early bug detection in the modern IC design cycle. The recent advancements in hardware fuzzing have proven effective in detecting bugs in RTL designs of cutting-edge processors. The modern IC design flow involves incremental updates and modifications to the hardware designs necessitating rigorous verification and extending the overall verification period. To …

ZK-WAGON: Imperceptible Watermark for Image Generation Models using ZK-SNARKs
Aadarsh Anantha Ramakrishnan, Shubham Agarwal, Selvanayagam S, Kunwar Singh
https://arxiv.org/abs/2510.01967

ZK-WAGON: Imperceptible Watermark for Image Generation Models using ZK-SNARKs
As image generation models grow increasingly powerful and accessible, concerns around authenticity, ownership, and misuse of synthetic media have become critical. The ability to generate lifelike images indistinguishable from real ones introduces risks such as misinformation, deepfakes, and intellectual property violations. Traditional watermarking methods either degrade image quality, are easily removed, or require access to confidential model internals - making them unsuitable for secure and …

Integrated Security Mechanisms for Weight Protection in Memristive Crossbar Arrays
Muhammad Faheemur Rahman, Wayne Burleson
https://arxiv.org/abs/2510.01350 https://

Integrated Security Mechanisms for Weight Protection in Memristive Crossbar Arrays
Memristive crossbar arrays enable in-memory computing by performing parallel analog computations directly within memory, making them well-suited for machine learning, neural networks, and neuromorphic systems. However, despite their advantages, non-volatile memristors are vulnerable to security threats (such as adversarial extraction of stored weights when the hardware is compromised. Protecting these weights is essential since they represent valuable intellectual property resulting from length…