Tootfinder

International Trade and Intellectual Property
Gaetan de Rassenfosse
https://arxiv.org/abs/2506.18929 https://arxiv.org/pdf/2506.18929…

International Trade and Intellectual Property
Intellectual property (IP) rules have the potential to shape cross-border trade far more than their legalistic origins might suggest. Drawing on three decades of evidence, this review shows that stronger IP rights simultaneously create market-power forces that raise prices and market-expansion forces that broaden demand, while dynamic incentives spur quality upgrading and new export varieties. Micro-data and quasi-natural experiments after TRIPS reveal that IP most often boosts trade along the …

Side-Channel Extraction of Dataflow AI Accelerator Hardware Parameters
Guillaume Lomet, Ruben Salvador, Brice Colombier, Vincent Grosso, Olivier Sentieys, Cedric Killian
https://arxiv.org/abs/2506.15432

Side-Channel Extraction of Dataflow AI Accelerator Hardware Parameters
Dataflow neural network accelerators efficiently process AI tasks on FPGAs, with deployment simplified by ready-to-use frameworks and pre-trained models. However, this convenience makes them vulnerable to malicious actors seeking to reverse engineer valuable Intellectual Property (IP) through Side-Channel Attacks (SCA). This paper proposes a methodology to recover the hardware configuration of dataflow accelerators generated with the FINN framework. Through unsupervised dimensionality reduction…

MEraser: An Effective Fingerprint Erasure Approach for Large Language Models
Jingxuan Zhang, Zhenhua Xu, Rui Hu, Wenpeng Xing, Xuhong Zhang, Meng Han
https://arxiv.org/abs/2506.12551

MEraser: An Effective Fingerprint Erasure Approach for Large Language Models
Large Language Models (LLMs) have become increasingly prevalent across various sectors, raising critical concerns about model ownership and intellectual property protection. Although backdoor-based fingerprinting has emerged as a promising solution for model authentication, effective attacks for removing these fingerprints remain largely unexplored. Therefore, we present Mismatched Eraser (MEraser), a novel method for effectively removing backdoor-based fingerprints from LLMs while maintaining …

Leider werden viele Leute keinen Widerspruch bei Meta eingelegt haben gegen die KI-Verwendung ihrer Facebook/Instagram-Daten, weil sie dachten "Boah, schon wieder so ein nutzloser Ich-widerspreche Post der nicht funktioniert."
Bin da wohl nicht ganz unschuldig dran, hab vor 11 Jahren einen Scherz gepostet, den viele nicht erkannten (siehe ab Zeile 10)
#Meta

WGLE:Backdoor-free and Multi-bit Black-box Watermarking for Graph Neural Networks
Tingzhi Li, Xuefeng Liu
https://arxiv.org/abs/2506.08602 https://

WGLE:Backdoor-free and Multi-bit Black-box Watermarking for Graph Neural Networks
Graph Neural Networks (GNNs) are increasingly deployed in graph-related applications, making ownership verification critical to protect their intellectual property against model theft. Fingerprinting and black-box watermarking are two main methods. However, the former relies on determining model similarity, which is computationally expensive and prone to ownership collisions after model post-processing such as model pruning or fine-tuning. The latter embeds backdoors, exposing watermarked model…

Stealix: Model Stealing via Prompt Evolution
Zhixiong Zhuang, Hui-Po Wang, Maria-Irina Nicolae, Mario Fritz
https://arxiv.org/abs/2506.05867 https://

Stealix: Model Stealing via Prompt Evolution
Model stealing poses a significant security risk in machine learning by enabling attackers to replicate a black-box model without access to its training data, thus jeopardizing intellectual property and exposing sensitive information. Recent methods that use pre-trained diffusion models for data synthesis improve efficiency and performance but rely heavily on manually crafted prompts, limiting automation and scalability, especially for attackers with little expertise. To assess the risks posed …

This https://arxiv.org/abs/2407.03883 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Protecting Deep Learning Model Copyrights with Adversarial Example-Free Reuse Detection
Model reuse techniques can reduce the resource requirements for training high-performance deep neural networks (DNNs) by leveraging existing models. However, unauthorized reuse and replication of DNNs can lead to copyright infringement and economic loss to the model owner. This underscores the need to analyze the reuse relation between DNNs and develop copyright protection techniques to safeguard intellectual property rights. Existing white-box testing-based approaches cannot address the common…

SoK: Are Watermarks in LLMs Ready for Deployment?
Kieu Dang, Phung Lai, NhatHai Phan, Yelong Shen, Ruoming Jin, Abdallah Khreishah, My Thai
https://arxiv.org/abs/2506.05594

SoK: Are Watermarks in LLMs Ready for Deployment?
Large Language Models (LLMs) have transformed natural language processing, demonstrating impressive capabilities across diverse tasks. However, deploying these models introduces critical risks related to intellectual property violations and potential misuse, particularly as adversaries can imitate these models to steal services or generate misleading outputs. We specifically focus on model stealing attacks, as they are highly relevant to proprietary LLMs and pose a serious threat to their secur…

This https://arxiv.org/abs/2505.04977 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

ChainMarks: Securing DNN Watermark with Cryptographic Chain
With the widespread deployment of deep neural network (DNN) models, dynamic watermarking techniques are being used to protect the intellectual property of model owners. However, recent studies have shown that existing watermarking schemes are vulnerable to watermark removal and ambiguity attacks. Besides, the vague criteria for determining watermark presence further increase the likelihood of such attacks. In this paper, we propose a secure DNN watermarking scheme named ChainMarks, which genera…

MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models
Xueqi Cheng, Minxing Zheng, Shixiang Zhu, Yushun Dong
https://arxiv.org/abs/2506.02362

MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models
Model extraction attacks aim to replicate the functionality of a black-box model through query access, threatening the intellectual property (IP) of machine-learning-as-a-service (MLaaS) providers. Defending against such attacks is challenging, as it must balance efficiency, robustness, and utility preservation in the real-world scenario. Despite the recent advances, most existing defenses presume that attacker queries have out-of-distribution (OOD) samples, enabling them to detect and disrupt …

KGMark: A Diffusion Watermark for Knowledge Graphs
Hongrui Peng, Haolang Lu, Yuanlong Yu, Weiye Fu, Kun Wang, Guoshun Nan
https://arxiv.org/abs/2505.23873 …

KGMark: A Diffusion Watermark for Knowledge Graphs
Knowledge graphs (KGs) are ubiquitous in numerous real-world applications, and watermarking facilitates protecting intellectual property and preventing potential harm from AI-generated content. Existing watermarking methods mainly focus on static plain text or image data, while they can hardly be applied to dynamic graphs due to spatial and temporal variations of structured data. This motivates us to propose KGMARK, the first graph watermarking framework that aims to generate robust, detectable…

CHIP: Chameleon Hash-based Irreversible Passport for Robust Deep Model Ownership Verification and Active Usage Control
Chaohui Xu, Qi Cui, Chip-Hong Chang
https://arxiv.org/abs/2505.24536

CHIP: Chameleon Hash-based Irreversible Passport for Robust Deep Model Ownership Verification and Active Usage Control
The pervasion of large-scale Deep Neural Networks (DNNs) and their enormous training costs make their intellectual property (IP) protection of paramount importance. Recently introduced passport-based methods attempt to steer DNN watermarking towards strengthening ownership verification against ambiguity attacks by modulating the affine parameters of normalization layers. Unfortunately, neither watermarking nor passport-based methods provide a holistic protection with robust ownership proof, hig…

Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning
Sayyed Farid Ahamed, Sandip Roy, Soumya Banerjee, Marc Vucovich, Kevin Choi, Abdul Rahman, Alison Hu, Edward Bowen, Sachin Shetty
https://arxiv.org/abs/2505.23791

Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning
Federated Learning (FL) is a collaborative learning framework designed to protect client data, yet it remains highly vulnerable to Intellectual Property (IP) threats. Model extraction (ME) attacks pose a significant risk to Machine Learning as a Service (MLaaS) platforms, enabling attackers to replicate confidential models by querying black-box (without internal insight) APIs. Despite FL's privacy-preserving goals, its distributed nature makes it particularly susceptible to such attacks. This p…

This https://arxiv.org/abs/2410.00059 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

IDEA: An Inverse Domain Expert Adaptation Based Active DNN IP Protection Method
Illegitimate reproduction, distribution and derivation of Deep Neural Network (DNN) models can inflict economic loss, reputation damage and even privacy infringement. Passive DNN intellectual property (IP) protection methods such as watermarking and fingerprinting attempt to prove the ownership upon IP violation, but they are often too late to stop catastrophic damage of IP abuse and too feeble against strong adversaries. In this paper, we propose IDEA, an Inverse Domain Expert Adaptation based…

This https://arxiv.org/abs/2409.04459 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

WET: Overcoming Paraphrasing Vulnerabilities in Embeddings-as-a-Service with Linear Transformation Watermarks
Embeddings-as-a-Service (EaaS) is a service offered by large language model (LLM) developers to supply embeddings generated by LLMs. Previous research suggests that EaaS is prone to imitation attacks -- attacks that clone the underlying EaaS model by training another model on the queried embeddings. As a result, EaaS watermarks are introduced to protect the intellectual property of EaaS providers. In this paper, we first show that existing EaaS watermarks can be removed by paraphrasing when att…