187 research outputs found
CNN-based fast source device identification
Source identification is an important topic in image forensics, since it
allows to trace back the origin of an image. This represents a precious
information to claim intellectual property but also to reveal the authors of
illicit materials. In this paper we address the problem of device
identification based on sensor noise and propose a fast and accurate solution
using convolutional neural networks (CNNs). Specifically, we propose a
2-channel-based CNN that learns a way of comparing camera fingerprint and image
noise at patch level. The proposed solution turns out to be much faster than
the conventional approach and to ensure an increased accuracy. This makes the
approach particularly suitable in scenarios where large databases of images are
analyzed, like over social networks. In this vein, since images uploaded on
social media usually undergo at least two compression stages, we include
investigations on double JPEG compressed images, always reporting higher
accuracy than standard approaches
Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision
Convolutional Neural Networks (CNNs) have proved very accurate in multiple
computer vision image classification tasks that required visual inspection in
the past (e.g., object recognition, face detection, etc.). Motivated by these
astonishing results, researchers have also started using CNNs to cope with
image forensic problems (e.g., camera model identification, tampering
detection, etc.). However, in computer vision, image classification methods
typically rely on visual cues easily detectable by human eyes. Conversely,
forensic solutions rely on almost invisible traces that are often very subtle
and lie in the fine details of the image under analysis. For this reason,
training a CNN to solve a forensic task requires some special care, as common
processing operations (e.g., resampling, compression, etc.) can strongly hinder
forensic traces. In this work, we focus on the effect that JPEG has on CNN
training considering different computer vision and forensic image
classification problems. Specifically, we consider the issues that rise from
JPEG compression and misalignment of the JPEG grid. We show that it is
necessary to consider these effects when generating a training dataset in order
to properly train a forensic detector not losing generalization capability,
whereas it is almost possible to ignore these effects for computer vision
tasks
DIPPAS: A Deep Image Prior PRNU Anonymization Scheme
Source device identification is an important topic in image forensics since
it allows to trace back the origin of an image. Its forensics counter-part is
source device anonymization, that is, to mask any trace on the image that can
be useful for identifying the source device. A typical trace exploited for
source device identification is the Photo Response Non-Uniformity (PRNU), a
noise pattern left by the device on the acquired images. In this paper, we
devise a methodology for suppressing such a trace from natural images without
significant impact on image quality. Specifically, we turn PRNU anonymization
into an optimization problem in a Deep Image Prior (DIP) framework. In a
nutshell, a Convolutional Neural Network (CNN) acts as generator and returns an
image that is anonymized with respect to the source PRNU, still maintaining
high visual quality. With respect to widely-adopted deep learning paradigms,
our proposed CNN is not trained on a set of input-target pairs of images.
Instead, it is optimized to reconstruct the PRNU-free image from the original
image under analysis itself. This makes the approach particularly suitable in
scenarios where large heterogeneous databases are analyzed and prevents any
problem due to lack of generalization. Through numerical examples on publicly
available datasets, we prove our methodology to be effective compared to
state-of-the-art techniques
Super-Resolution of BVOC Emission Maps Via Domain Adaptation
Enhancing the resolution of Biogenic Volatile Organic Compound (BVOC)
emission maps is a critical task in remote sensing. Recently, some
Super-Resolution (SR) methods based on Deep Learning (DL) have been proposed,
leveraging data from numerical simulations for their training process. However,
when dealing with data derived from satellite observations, the reconstruction
is particularly challenging due to the scarcity of measurements to train SR
algorithms with. In our work, we aim at super-resolving low resolution emission
maps derived from satellite observations by leveraging the information of
emission maps obtained through numerical simulations. To do this, we combine a
SR method based on DL with Domain Adaptation (DA) techniques, harmonizing the
different aggregation strategies and spatial information used in simulated and
observed domains to ensure compatibility. We investigate the effectiveness of
DA strategies at different stages by systematically varying the number of
simulated and observed emissions used, exploring the implications of data
scarcity on the adaptation strategies. To the best of our knowledge, there are
no prior investigations of DA in satellite-derived BVOC maps enhancement. Our
work represents a first step toward the development of robust strategies for
the reconstruction of observed BVOC emissions.Comment: 4 pages, 4 figures, 1 table, accepted at IEEE-IGARSS 202
Enhancing Biogenic Emission Maps Using Deep Learning
Biogenic Volatile Organic Compounds (BVOCs) play a critical role in
biosphere-atmosphere interactions, being a key factor in the physical and
chemical properties of the atmosphere and climate. Acquiring large and
fine-grained BVOC emission maps is expensive and time-consuming, so most of the
available BVOC data are obtained on a loose and sparse sampling grid or on
small regions. However, high-resolution BVOC data are desirable in many
applications, such as air quality, atmospheric chemistry, and climate
monitoring. In this work, we propose to investigate the possibility of
enhancing BVOC acquisitions, taking a step forward in explaining the
relationships between plants and these compounds. We do so by comparing the
performances of several state-of-the-art neural networks proposed for
Single-Image Super-Resolution (SISR), showing how to adapt them to correctly
handle emission data through preprocessing. Moreover, we also consider
realistic scenarios, considering both temporal and geographical constraints.
Finally, we present possible future developments in terms of Super-Resolution
(SR) generalization, considering the scale-invariance property and
super-resolving emissions from unseen compounds.Comment: 5 pages, 4 figures, 3 table
Multi-BVOC Super-Resolution Exploiting Compounds Inter-Connection
Biogenic Volatile Organic Compounds (BVOCs) emitted from the terrestrial
ecosystem into the Earth's atmosphere are an important component of atmospheric
chemistry. Due to the scarcity of measurement, a reliable enhancement of BVOCs
emission maps can aid in providing denser data for atmospheric chemical,
climate, and air quality models. In this work, we propose a strategy to
super-resolve coarse BVOC emission maps by simultaneously exploiting the
contributions of different compounds. To this purpose, we first accurately
investigate the spatial inter-connections between several BVOC species. Then,
we exploit the found similarities to build a Multi-Image Super-Resolution
(MISR) system, in which a number of emission maps associated with diverse
compounds are aggregated to boost Super-Resolution (SR) performance. We compare
different configurations regarding the species and the number of joined BVOCs.
Our experimental results show that incorporating BVOCs' relationship into the
process can substantially improve the accuracy of the super-resolved maps.
Interestingly, the best results are achieved when we aggregate the emission
maps of strongly uncorrelated compounds. This peculiarity seems to confirm what
was already guessed for other data-domains, i.e., joined uncorrelated
information are more helpful than correlated ones to boost MISR performance.
Nonetheless, the proposed work represents the first attempt in SR of BVOC
emissions through the fusion of multiple different compounds.Comment: 5 pages, 4 figures, 1 table, accepted at EURASIP-EUSIPCO 202
Deep Image Prior Amplitude SAR Image Anonymization
This paper presents an extensive evaluation of the Deep Image Prior (DIP) technique for image inpainting on Synthetic Aperture Radar (SAR) images. SAR images are gaining popularity in various applications, but there may be a need to conceal certain regions of them. Image inpainting provides a solution for this. However, not all inpainting techniques are designed to work on SAR images. Some are intended for use on photographs, while others have to be specifically trained on top of a huge set of images. In this work, we evaluate the performance of the DIP technique that is capable of addressing these challenges: it can adapt to the image under analysis including SAR imagery; it does not require any training. Our results demonstrate that the DIP method achieves great performance in terms of objective and semantic metrics. This indicates that the DIP method is a promising approach for inpainting SAR images, and can provide high-quality results that meet the requirements of various applications
A Modified Fourier-Mellin Approach for Source Device Identification on Stabilized Videos
To decide whether a digital video has been captured by a given device,
multimedia forensic tools usually exploit characteristic noise traces left by
the camera sensor on the acquired frames. This analysis requires that the noise
pattern characterizing the camera and the noise pattern extracted from video
frames under analysis are geometrically aligned. However, in many practical
scenarios this does not occur, thus a re-alignment or synchronization has to be
performed. Current solutions often require time consuming search of the
realignment transformation parameters. In this paper, we propose to overcome
this limitation by searching scaling and rotation parameters in the frequency
domain. The proposed algorithm tested on real videos from a well-known
state-of-the-art dataset shows promising results
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