205 research outputs found
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
Deep Audio Analyzer: a Framework to Industrialize the Research on Audio Forensics
Deep Audio Analyzer is an open source speech framework that aims to simplify
the research and the development process of neural speech processing pipelines,
allowing users to conceive, compare and share results in a fast and
reproducible way. This paper describes the core architecture designed to
support several tasks of common interest in the audio forensics field, showing
possibility of creating new tasks thus customizing the framework. By means of
Deep Audio Analyzer, forensics examiners (i.e. from Law Enforcement Agencies)
and researchers will be able to visualize audio features, easily evaluate
performances on pretrained models, to create, export and share new audio
analysis workflows by combining deep neural network models with few clicks. One
of the advantages of this tool is to speed up research and practical
experimentation, in the field of audio forensics analysis thus also improving
experimental reproducibility by exporting and sharing pipelines. All features
are developed in modules accessible by the user through a Graphic User
Interface. Index Terms: Speech Processing, Deep Learning Audio, Deep Learning
Audio Pipeline creation, Audio Forensics
Animated GIF optimization by adaptive color local table management
After thirty years of the GIF file format, today is becoming more popular
than ever: being a great way of communication for friends and communities on
Instant Messengers and Social Networks. While being so popular, the original
compression method to encode GIF images have not changed a bit. On the other
hand popularity means that storage saving becomes an issue for hosting
platforms. In this paper a parametric optimization technique for animated GIFs
will be presented. The proposed technique is based on Local Color Table
selection and color remapping in order to create optimized animated GIFs while
preserving the original format. The technique achieves good results in terms of
byte reduction with limited or no loss of perceived color quality. Tests
carried out on 1000 GIF files demonstrate the effectiveness of the proposed
optimization strategy
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