Classification of Inkjet Printers based on Droplet Statistics
- URL: http://arxiv.org/abs/2407.09539v1
- Date: Wed, 26 Jun 2024 10:20:01 GMT
- Title: Classification of Inkjet Printers based on Droplet Statistics
- Authors: Patrick Takenaka, Manuel Eberhardinger, Daniel Grießhaber, Johannes Maucher,
- Abstract summary: Knowing the printer model used to print a given document may provide a crucial lead towards identifying counterfeits or verifying the validity of a real document.
We investigate the utilization of droplet characteristics including frequency domain features extracted from printed document scans for the classification of the underlying printer model.
- Score: 1.237454174824584
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Knowing the printer model used to print a given document may provide a crucial lead towards identifying counterfeits or conversely verifying the validity of a real document. Inkjet printers produce probabilistic droplet patterns that appear to be distinct for each printer model and as such we investigate the utilization of droplet characteristics including frequency domain features extracted from printed document scans for the classification of the underlying printer model. We collect and publish a dataset of high resolution document scans and show that our extracted features are informative enough to enable a neural network to distinguish not only the printer manufacturer, but also individual printer models.
Related papers
- Reduced-order modeling and classification of hydrodynamic pattern formation in gravure printing [2.4344640336100936]
Hydrodynamic pattern formation phenomena in printing and coating processes are still not fully understood.
The aim of the paper is to develop an automated pattern classification algorithm based on supervised machine learning and reduced-order modeling.
arXiv Detail & Related papers (2025-01-24T16:26:20Z) - 3D object quality prediction for Metal Jet Printer with Multimodal thermal encoder [46.85584046139531]
Various factors during metal printing affect the printed parts' quality.
With the large data gathered from HP's MetJet printing process, AI techniques can be used to analyze, learn, and effectively infer the printed part quality metrics.
arXiv Detail & Related papers (2024-04-17T21:57:29Z) - LCM-Lookahead for Encoder-based Text-to-Image Personalization [82.56471486184252]
We explore the potential of using shortcut-mechanisms to guide the personalization of text-to-image models.
We focus on encoder-based personalization approaches, and demonstrate that by tuning them with a lookahead identity loss, we can achieve higher identity fidelity.
arXiv Detail & Related papers (2024-04-04T17:43:06Z) - Improving and Evaluating Machine Learning Methods for Forensic Shoeprint Matching [0.2509487459755192]
We propose a machine learning pipeline for forensic shoeprint pattern matching.
We extract 2D coordinates from shoeprint scans using edge detection and align the two shoeprints with iterative closest point (ICP)
We then extract similarity metrics to quantify how well the two prints match and use these metrics to train a random forest.
arXiv Detail & Related papers (2024-04-02T15:24:25Z) - TokenMark: A Modality-Agnostic Watermark for Pre-trained Transformers [67.57928750537185]
TokenMark is a robust, modality-agnostic, robust watermarking system for pre-trained models.
It embeds the watermark by fine-tuning the pre-trained model on a set of specifically permuted data samples.
It significantly improves the robustness, efficiency, and universality of model watermarking.
arXiv Detail & Related papers (2024-03-09T08:54:52Z) - Contrastive Attention Networks for Attribution of Early Modern Print [23.344655278038392]
We develop machine learning techniques to identify unknown printers in early modern (c.1500--1800) English printed books.
Specifically, we focus on matching uniquely damaged character type-imprints in anonymously printed books to works with known printers.
arXiv Detail & Related papers (2023-06-12T19:57:11Z) - Sampling and Ranking for Digital Ink Generation on a tight computational
budget [69.15275423815461]
We study ways to maximize the quality of the output of a trained digital ink generative model.
We use and compare the effect of multiple sampling and ranking techniques, in the first ablation study of its kind in the digital ink domain.
arXiv Detail & Related papers (2023-06-02T09:55:15Z) - Watermarking for Out-of-distribution Detection [76.20630986010114]
Out-of-distribution (OOD) detection aims to identify OOD data based on representations extracted from well-trained deep models.
We propose a general methodology named watermarking in this paper.
We learn a unified pattern that is superimposed onto features of original data, and the model's detection capability is largely boosted after watermarking.
arXiv Detail & Related papers (2022-10-27T06:12:32Z) - Incorporating Vision Bias into Click Models for Image-oriented Search
Engine [51.192784793764176]
In this paper, we assume that vision bias exists in an image-oriented search engine as another crucial factor affecting the examination probability aside from position.
We use regression-based EM algorithm to predict the vision bias given the visual features extracted from candidate documents.
arXiv Detail & Related papers (2021-01-07T10:01:31Z) - Responsible Disclosure of Generative Models Using Scalable
Fingerprinting [70.81987741132451]
Deep generative models have achieved a qualitatively new level of performance.
There are concerns on how this technology can be misused to spoof sensors, generate deep fakes, and enable misinformation at scale.
Our work enables a responsible disclosure of such state-of-the-art generative models, that allows researchers and companies to fingerprint their models.
arXiv Detail & Related papers (2020-12-16T03:51:54Z) - Printing and Scanning Attack for Image Counter Forensics [11.193867567895353]
Examining the authenticity of images has become increasingly important as manipulation tools become more accessible and advanced.
Recent work has shown that while CNN-based image manipulation detectors can successfully identify manipulations, they are also vulnerable to adversarial attacks.
We explore another method of highly plausible attack: printing and scanning.
arXiv Detail & Related papers (2020-04-27T00:32:15Z) - Source Printer Identification from Document Images Acquired using
Smartphone [14.889347839830092]
We propose to learn a single CNN model from the fusion of letter images and their printer-specific noise residuals.
The proposed method achieves 98.42% document classification accuracy using images of letter 'e' under a 5x2 cross-validation approach.
arXiv Detail & Related papers (2020-03-27T18:59:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.