187 research outputs found
Innovative Methods for Non-Destructive Inspection of Handwritten Documents
Handwritten document analysis is an area of forensic science, with the goal
of establishing authorship of documents through examination of inherent
characteristics. Law enforcement agencies use standard protocols based on
manual processing of handwritten documents. This method is time-consuming, is
often subjective in its evaluation, and is not replicable. To overcome these
limitations, in this paper we present a framework capable of extracting and
analyzing intrinsic measures of manuscript documents related to text line
heights, space between words, and character sizes using image processing and
deep learning techniques. The final feature vector for each document involved
consists of the mean and standard deviation for every type of measure
collected. By quantifying the Euclidean distance between the feature vectors of
the documents to be compared, authorship can be discerned. Our study pioneered
the comparison between traditionally handwritten documents and those produced
with digital tools (e.g., tablets). Experimental results demonstrate the
ability of our method to objectively determine authorship in different writing
media, outperforming the state of the art
Deepfake Style Transfer Mixture: a First Forensic Ballistics Study on Synthetic Images
Most recent style-transfer techniques based on generative architectures are
able to obtain synthetic multimedia contents, or commonly called deepfakes,
with almost no artifacts. Researchers already demonstrated that synthetic
images contain patterns that can determine not only if it is a deepfake but
also the generative architecture employed to create the image data itself.
These traces can be exploited to study problems that have never been addressed
in the context of deepfakes. To this aim, in this paper a first approach to
investigate the image ballistics on deepfake images subject to style-transfer
manipulations is proposed. Specifically, this paper describes a study on
detecting how many times a digital image has been processed by a generative
architecture for style transfer. Moreover, in order to address and study
accurately forensic ballistics on deepfake images, some mathematical properties
of style-transfer operations were investigated
A Novel Dataset for Non-Destructive Inspection of Handwritten Documents
Forensic handwriting examination is a branch of Forensic Science that aims to
examine handwritten documents in order to properly define or hypothesize the
manuscript's author. These analysis involves comparing two or more (digitized)
documents through a comprehensive comparison of intrinsic local and global
features. If a correlation exists and specific best practices are satisfied,
then it will be possible to affirm that the documents under analysis were
written by the same individual. The need to create sophisticated tools capable
of extracting and comparing significant features has led to the development of
cutting-edge software with almost entirely automated processes, improving the
forensic examination of handwriting and achieving increasingly objective
evaluations. This is made possible by algorithmic solutions based on purely
mathematical concepts. Machine Learning and Deep Learning models trained with
specific datasets could turn out to be the key elements to best solve the task
at hand. In this paper, we proposed a new and challenging dataset consisting of
two subsets: the first consists of 21 documents written either by the classic
``pen and paper" approach (and later digitized) and directly acquired on common
devices such as tablets; the second consists of 362 handwritten manuscripts by
124 different people, acquired following a specific pipeline. Our study
pioneered a comparison between traditionally handwritten documents and those
produced with digital tools (e.g., tablets). Preliminary results on the
proposed datasets show that 90% classification accuracy can be achieved on the
first subset (documents written on both paper and pen and later digitized and
on tablets) and 96% on the second portion of the data. The datasets are
available at
https://iplab.dmi.unict.it/mfs/forensic-handwriting-analysis/novel-dataset-2023/.Comment: arXiv admin note: text overlap with arXiv:2310.1121
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