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from my link log —
JA4 : a suite of network fingerprinting standards.
https://github.com/FoxIO-LLC/ja4
saved 2025-03-07 https://d…

GitHub - FoxIO-LLC/ja4: JA4+ is a suite of network fingerprinting standards
JA4+ is a suite of network fingerprinting standards - FoxIO-LLC/ja4

Unlearning-Enhanced Website Fingerprinting Attack: Against Backdoor Poisoning in Anonymous Networks
Yali Yuan, Kai Xu, Ruolin Ma, Yuchen Zhang
https://arxiv.org/abs/2506.13563

Unlearning-Enhanced Website Fingerprinting Attack: Against Backdoor Poisoning in Anonymous Networks
Website Fingerprinting (WF) is an effective tool for regulating and governing the dark web. However, its performance can be significantly degraded by backdoor poisoning attacks in practical deployments. This paper aims to address the problem of hidden backdoor poisoning attacks faced by Website Fingerprinting attack, and designs a feasible mothed that integrates unlearning technology to realize detection of automatic poisoned points and complete removal of its destructive effects, requiring onl…

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 …

Adaptive Gate-Aware Mamba Networks for Magnetic Resonance Fingerprinting
Tianyi Ding, Hongli Chen, Yang Gao, Zhuang Xiong, Feng Liu, Martijn A. Cloos, Hongfu Sun
https://arxiv.org/abs/2507.03369

Adaptive Gate-Aware Mamba Networks for Magnetic Resonance Fingerprinting
Magnetic Resonance Fingerprinting (MRF) enables fast quantitative imaging by matching signal evolutions to a predefined dictionary. However, conventional dictionary matching suffers from exponential growth in computational cost and memory usage as the number of parameters increases, limiting its scalability to multi-parametric mapping. To address this, recent work has explored deep learning-based approaches as alternatives to DM. We propose GAST-Mamba, an end-to-end framework that combines a du…

Connectome brain fingerprinting: terminology, measures, and target properties
Matteo Fraschini, Matteo Demuru, Daniele Marinazzo, Luca Didaci
https://arxiv.org/abs/2506.05769

Connectome brain fingerprinting: terminology, measures, and target properties
Distinguishing one person from another (what biometricians call recognition) is extremely relevant for different aspects of life. Traditional biometric modalities (fingerprint, face, iris, voice) rely on unique, stable features that reliably differentiate individuals. Recently, the term fingerprinting has gained popularity in neuroscience, with a growing number of studies adopting the term to describe various brain based metrics derived from different techniques. However, we think there is a mi…

Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol
Christos Nikou, Theodoros Giannakopoulos
https://arxiv.org/abs/2507.06070

Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol
Recent advances in song identification leverage deep neural networks to learn compact audio fingerprints directly from raw waveforms. While these methods perform well under controlled conditions, their accuracy drops significantly in real-world scenarios where the audio is captured via mobile devices in noisy environments. In this paper, we introduce a novel evaluation protocol designed to better reflect such real-world conditions. We generate three recordings of the same audio, each with incre…

Physical Layer-Based Device Fingerprinting for Wireless Security: From Theory to Practice
Junqing Zhang, Francesco Ardizzon, Mattia Piana, Guanxiong Shen, Stefano Tomasin
https://arxiv.org/abs/2506.09807

Physical Layer-Based Device Fingerprinting for Wireless Security: From Theory to Practice
The identification of the devices from which a message is received is part of security mechanisms to ensure authentication in wireless communications. Conventional authentication approaches are cryptography-based, which, however, are usually computationally expensive and not adequate in the Internet of Things (IoT), where devices tend to be low-cost and with limited resources. This paper provides a comprehensive survey of physical layer-based device fingerprinting, which is an emerging device a…

One task to rule them all: A closer look at traffic classification generalizability
Elham Akbari, Zihao Zhou, Mohammad Ali Salahuddin, Noura Limam, Raouf Boutaba, Bertrand Mathieu, Stephanie Moteau, Stephane Tuffin
https://arxiv.org/abs/2507.06430

One task to rule them all: A closer look at traffic classification generalizability
Existing website fingerprinting and traffic classification solutions do not work well when the evaluation context changes, as their performances often heavily rely on context-specific assumptions. To clarify this problem, we take three prior solutions presented for different but similar traffic classification and website fingerprinting tasks, and apply each solution's model to another solution's dataset. We pinpoint dataset-specific and model-specific properties that lead each of them to overpe…

In West-Nordamerika ist der Einfluss des Menschen auf das #Feuerwetter (heißes, trockenes, windreiches Wetter) jetzt messbar.
Zwischen 81 % und 188 % des Anstiegs in den letzten 50 Jahren gehen auf menschgemachte #Klimaveränderungen zurück.
Temperatur,

Fingerprint of anthropogenic climate change detected in long-term western North American fire weather trends - Communications Earth & Environment
Anthropogenic climate change has been the dominant driver of increasing fire weather trends in western North America over the past 50 years, contributing 81–188% of the observed linear trends, according to regional optimal fingerprinting applied to large ensembles of high-resolution climate models.

Polynomial fingerprinting for trees and formulas
Mihai Prunescu
https://arxiv.org/abs/2506.21114 https://arxiv.org/pdf/2506.21114

Polynomial fingerprinting for trees and formulas
To cater to the needs of (Zero Knowledge) proofs for (mathematical) proofs, we describe a method to transform formal sentences in 2x2 - matrices over multivariate polynomials with integer coefficients, such that usual proof-steps like modus-ponens or the substitution are easy to compute from the matrices corresponding to the terms or formulas used as arguments. By evaluating the polynomial variables in random elements of a suitably chosen finite field, the proof is replaced by a numeric sequenc…

SATversary: Adversarial Attacks on Satellite Fingerprinting
Joshua Smailes, Sebastian K\"ohler, Simon Birnbach, Martin Strohmeier, Ivan Martinovic
https://arxiv.org/abs/2506.06119

SATversary: Adversarial Attacks on Satellite Fingerprinting
As satellite systems become increasingly vulnerable to physical layer attacks via SDRs, novel countermeasures are being developed to protect critical systems, particularly those lacking cryptographic protection, or those which cannot be upgraded to support modern cryptography. Among these is transmitter fingerprinting, which provides mechanisms by which communication can be authenticated by looking at characteristics of the transmitter, expressed as impairments on the signal. Previous works s…

Deep ultraviolet resonant Raman (DUVRR) spectroscopy for spectroscopic evaluation and disinfection of food and agricultural samples
Joseph T. Harrington, Vsevolod Cheburkanov, Mykyta Kizilov, Ilya Kulagin, Georgi Petrov, Vladislav V. Yakovlev
https://arxiv.org/abs/2507.04199

Deep ultraviolet resonant Raman (DUVRR) spectroscopy for spectroscopic evaluation and disinfection of food and agricultural samples
The increasing demands on modern plant and food production due to climate change, regulatory pressures, and the Sustainable Development Goals necessitate advanced photonic technologies for improved sustainability. Deep ultraviolet resonant Raman (DUVRR) spectroscopy offers precise spectral fingerprinting and potential disinfection capabilities, making it a promising tool for agricultural and food sciences. We developed a cost-effective, portable DUVRR spectroscopy system using a mercury (Hg) la…

Dynamical Multimodal Fusion with Mixture-of-Experts for Localizations
Bohao Wang, Zitao Shuai, Fenghao Zhu, Chongwen Huang, Yongliang Shen, Zhaoyang Zhang, Qianqian Yang, Sami Muhaidat, Merouane Debbah
https://arxiv.org/abs/2507.01337

Dynamical Multimodal Fusion with Mixture-of-Experts for Localizations
Multimodal fingerprinting is a crucial technique to sub-meter 6G integrated sensing and communications (ISAC) localization, but two hurdles block deployment: (i) the contribution each modality makes to the target position varies with the operating conditions such as carrier frequency, and (ii) spatial and fingerprint ambiguities markedly undermine localization accuracy, especially in non-line-of-sight (NLOS) scenarios. To solve these problems, we introduce SCADF-MoE, a spatial-context aware dyn…

PeakNetFP: Peak-based Neural Audio Fingerprinting Robust to Extreme Time Stretching
Guillem Cort\`es-Sebasti\`a, Benjamin Martin, Emilio Molina, Xavier Serra, Romain Hennequin
https://arxiv.org/abs/2506.21086

PeakNetFP: Peak-based Neural Audio Fingerprinting Robust to Extreme Time Stretching
This work introduces PeakNetFP, the first neural audio fingerprinting (AFP) system designed specifically around spectral peaks. This novel system is designed to leverage the sparse spectral coordinates typically computed by traditional peak-based AFP methods. PeakNetFP performs hierarchical point feature extraction techniques similar to the computer vision model PointNet++, and is trained using contrastive learning like in the state-of-the-art deep learning AFP, NeuralFP. This combination allow…

Benchmarking Large Language Models for Polymer Property Predictions
Sonakshi Gupta, Akhlak Mahmood, Shivank Shukla, Rampi Ramprasad
https://arxiv.org/abs/2506.02129

Benchmarking Large Language Models for Polymer Property Predictions
Machine learning has revolutionized polymer science by enabling rapid property prediction and generative design. Large language models (LLMs) offer further opportunities in polymer informatics by simplifying workflows that traditionally rely on large labeled datasets, handcrafted representations, and complex feature engineering. LLMs leverage natural language inputs through transfer learning, eliminating the need for explicit fingerprinting and streamlining training. In this study, we finetune …

Hardware acceleration for ultra-fast Neural Network training on FPGA for MRF map reconstruction
Mattia Ricchi, Fabrizio Alfonsi, Camilla Marella, Marco Barbieri, Alessandra Retico, Leonardo Brizi, Alessandro Gabrielli, Claudia Testa
https://arxiv.org/abs/2506.22156

Hardware acceleration for ultra-fast Neural Network training on FPGA for MRF map reconstruction
Magnetic Resonance Fingerprinting (MRF) is a fast quantitative MR Imaging technique that provides multi-parametric maps with a single acquisition. Neural Networks (NNs) accelerate reconstruction but require significant resources for training. We propose an FPGA-based NN for real-time brain parameter reconstruction from MRF data. Training the NN takes an estimated 200 seconds, significantly faster than standard CPU-based training, which can be up to 250 times slower. This method could enable rea…

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…

Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction
Perla Mayo, Carolin M. Pirkl, Alin Achim, Bjoern Menze, Mohammad Golbabaee
https://arxiv.org/abs/2506.23311

Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction
We introduce MRF-DiPh, a novel physics informed denoising diffusion approach for multiparametric tissue mapping from highly accelerated, transient-state quantitative MRI acquisitions like Magnetic Resonance Fingerprinting (MRF). Our method is derived from a proximal splitting formulation, incorporating a pretrained denoising diffusion model as an effective image prior to regularize the MRF inverse problem. Further, during reconstruction it simultaneously enforces two key physical constraints: (…

Attack Smarter: Attention-Driven Fine-Grained Webpage Fingerprinting Attacks
Yali Yuan, Weiyi Zou, Guang Cheng
https://arxiv.org/abs/2506.20082 https://

Attack Smarter: Attention-Driven Fine-Grained Webpage Fingerprinting Attacks
Website Fingerprinting (WF) attacks aim to infer which websites a user is visiting by analyzing traffic patterns, thereby compromising user anonymity. Although this technique has been demonstrated to be effective in controlled experimental environments, it remains largely limited to small-scale scenarios, typically restricted to recognizing website homepages. In practical settings, however, users frequently access multiple subpages in rapid succession, often before previous content fully loads.…

Walking Fingerprinting Using Wrist Accelerometry During Activities of Daily Living in NHANES
Lily Koffman, John Muschelli III, Ciprian Crainiceanu
https://arxiv.org/abs/2506.17160

Walking Fingerprinting Using Wrist Accelerometry During Activities of Daily Living in NHANES
We propose a method for identifying individuals based on their continuously monitored wrist-worn accelerometry during activities of daily living. The method consists of three steps: (1) using Adaptive Empirical Pattern Transformation (ADEPT), a highly specific method to identify walking; (2) transforming the accelerometry time series into an image that corresponds to the joint distribution of the time series and its lags; and (3) using the resulting images to construct a person-specific walking…

Unlearning-Enhanced Website Fingerprinting Attack: Against Backdoor Poisoning in Anonymous Networks
Yali Yuan, Kai Xu, Ruolin Ma, Yuchen Zhang
https://arxiv.org/abs/2506.13563

Unlearning-Enhanced Website Fingerprinting Attack: Against Backdoor Poisoning in Anonymous Networks
Website Fingerprinting (WF) is an effective tool for regulating and governing the dark web. However, its performance can be significantly degraded by backdoor poisoning attacks in practical deployments. This paper aims to address the problem of hidden backdoor poisoning attacks faced by Website Fingerprinting attack, and designs a feasible mothed that integrates unlearning technology to realize detection of automatic poisoned points and complete removal of its destructive effects, requiring onl…

Enhancing Neural Audio Fingerprint Robustness to Audio Degradation for Music Identification
R. O. Araz, G. Cortes-Sebastia, E. Molina, J. Serra, X. Serra, Y. Mitsufuji, D. Bogdanov
https://arxiv.org/abs/2506.22661

Enhancing Neural Audio Fingerprint Robustness to Audio Degradation for Music Identification
Audio fingerprinting (AFP) allows the identification of unknown audio content by extracting compact representations, termed audio fingerprints, that are designed to remain robust against common audio degradations. Neural AFP methods often employ metric learning, where representation quality is influenced by the nature of the supervision and the utilized loss function. However, recent work unrealistically simulates real-life audio degradation during training, resulting in sub-optimal supervision…

Intrinsic Fingerprint of LLMs: Continue Training is NOT All You Need to Steal A Model!
Do-hyeon Yoon, Minsoo Chun, Thomas Allen, Hans M\"uller, Min Wang, Rajesh Sharma
https://arxiv.org/abs/2507.03014

Intrinsic Fingerprint of LLMs: Continue Training is NOT All You Need to Steal A Model!
Large language models (LLMs) face significant copyright and intellectual property challenges as the cost of training increases and model reuse becomes prevalent. While watermarking techniques have been proposed to protect model ownership, they may not be robust to continue training and development, posing serious threats to model attribution and copyright protection. This work introduces a simple yet effective approach for robust LLM fingerprinting based on intrinsic model characteristics. We d…

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…

Refining music sample identification with a self-supervised graph neural network
Aditya Bhattacharjee, Ivan Meresman Higgs, Mark Sandler, Emmanouil Benetos
https://arxiv.org/abs/2506.14684

Refining music sample identification with a self-supervised graph neural network
Automatic sample identification (ASID), the detection and identification of portions of audio recordings that have been reused in new musical works, is an essential but challenging task in the field of audio query-based retrieval. While a related task, audio fingerprinting, has made significant progress in accurately retrieving musical content under "real world" (noisy, reverberant) conditions, ASID systems struggle to identify samples that have undergone musical modifications. Thus, a system r…

Bayesian Perspective on Memorization and Reconstruction
Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer
https://arxiv.org/abs/2505.23658 https://

Bayesian Perspective on Memorization and Reconstruction
We introduce a new Bayesian perspective on the concept of data reconstruction, and leverage this viewpoint to propose a new security definition that, in certain settings, provably prevents reconstruction attacks. We use our paradigm to shed new light on one of the most notorious attacks in the privacy and memorization literature - fingerprinting code attacks (FPC). We argue that these attacks are really a form of membership inference attacks, rather than reconstruction attacks. Furthermore, we …