75 research outputs found
Exposing Fake Images with Forensic Similarity Graphs
We propose new image forgery detection and localization algorithms by
recasting these problems as graph-based community detection problems. To do
this, we introduce a novel abstract, graph-based representation of an image,
which we call the Forensic Similarity Graph, that captures key forensic
relationships among regions in the image. In this representation, small image
patches are represented by graph vertices with edges assigned according to the
forensic similarity between patches. Localized tampering introduces unique
structure into this graph, which aligns with a concept called ``community
structure'' in graph-theory literature. In the Forensic Similarity Graph,
communities correspond to the tampered and unaltered regions in the image. As a
result, forgery detection is performed by identifying whether multiple
communities exist, and forgery localization is performed by partitioning these
communities. We present two community detection techniques, adapted from
literature, to detect and localize image forgeries. We experimentally show that
our proposed community detection methods outperform existing state-of-the-art
forgery detection and localization methods, which do not capture such community
structure.Comment: 16 pages, under review at IEEE Journal of Selected Topics in Signal
Processin
Attacking Image Splicing Detection and Localization Algorithms Using Synthetic Traces
Recent advances in deep learning have enabled forensics researchers to
develop a new class of image splicing detection and localization algorithms.
These algorithms identify spliced content by detecting localized
inconsistencies in forensic traces using Siamese neural networks, either
explicitly during analysis or implicitly during training. At the same time,
deep learning has enabled new forms of anti-forensic attacks, such as
adversarial examples and generative adversarial network (GAN) based attacks.
Thus far, however, no anti-forensic attack has been demonstrated against image
splicing detection and localization algorithms. In this paper, we propose a new
GAN-based anti-forensic attack that is able to fool state-of-the-art splicing
detection and localization algorithms such as EXIF-Net, Noiseprint, and
Forensic Similarity Graphs. This attack operates by adversarially training an
anti-forensic generator against a set of Siamese neural networks so that it is
able to create synthetic forensic traces. Under analysis, these synthetic
traces appear authentic and are self-consistent throughout an image. Through a
series of experiments, we demonstrate that our attack is capable of fooling
forensic splicing detection and localization algorithms without introducing
visually detectable artifacts into an attacked image. Additionally, we
demonstrate that our attack outperforms existing alternative attack approaches.
Open Set Synthetic Image Source Attribution
AI-generated images have become increasingly realistic and have garnered
significant public attention. While synthetic images are intriguing due to
their realism, they also pose an important misinformation threat. To address
this new threat, researchers have developed multiple algorithms to detect
synthetic images and identify their source generators. However, most existing
source attribution techniques are designed to operate in a closed-set scenario,
i.e. they can only be used to discriminate between known image generators. By
contrast, new image-generation techniques are rapidly emerging. To contend with
this, there is a great need for open-set source attribution techniques that can
identify when synthetic images have originated from new, unseen generators. To
address this problem, we propose a new metric learning-based approach. Our
technique works by learning transferrable embeddings capable of discriminating
between generators, even when they are not seen during training. An image is
first assigned to a candidate generator, then is accepted or rejected based on
its distance in the embedding space from known generators' learned reference
points. Importantly, we identify that initializing our source attribution
embedding network by pretraining it on image camera identification can improve
our embeddings' transferability. Through a series of experiments, we
demonstrate our approach's ability to attribute the source of synthetic images
in open-set scenarios
VideoFACT: Detecting Video Forgeries Using Attention, Scene Context, and Forensic Traces
Fake videos represent an important misinformation threat. While existing
forensic networks have demonstrated strong performance on image forgeries,
recent results reported on the Adobe VideoSham dataset show that these networks
fail to identify fake content in videos. In this paper, we show that this is
due to video coding, which introduces local variation into forensic traces. In
response, we propose VideoFACT - a new network that is able to detect and
localize a wide variety of video forgeries and manipulations. To overcome
challenges that existing networks face when analyzing videos, our network
utilizes both forensic embeddings to capture traces left by manipulation,
context embeddings to control for variation in forensic traces introduced by
video coding, and a deep self-attention mechanism to estimate the quality and
relative importance of local forensic embeddings. We create several new video
forgery datasets and use these, along with publicly available data, to
experimentally evaluate our network's performance. These results show that our
proposed network is able to identify a diverse set of video forgeries,
including those not encountered during training. Furthermore, we show that our
network can be fine-tuned to achieve even stronger performance on challenging
AI-based manipulations
TIMIT-TTS: a Text-to-Speech Dataset for Multimodal Synthetic Media Detection
With the rapid development of deep learning techniques, the generation and
counterfeiting of multimedia material are becoming increasingly straightforward
to perform. At the same time, sharing fake content on the web has become so
simple that malicious users can create unpleasant situations with minimal
effort. Also, forged media are getting more and more complex, with manipulated
videos that are taking the scene over still images. The multimedia forensic
community has addressed the possible threats that this situation could imply by
developing detectors that verify the authenticity of multimedia objects.
However, the vast majority of these tools only analyze one modality at a time.
This was not a problem as long as still images were considered the most widely
edited media, but now, since manipulated videos are becoming customary,
performing monomodal analyses could be reductive. Nonetheless, there is a lack
in the literature regarding multimodal detectors, mainly due to the scarsity of
datasets containing forged multimodal data to train and test the designed
algorithms. In this paper we focus on the generation of an audio-visual
deepfake dataset. First, we present a general pipeline for synthesizing speech
deepfake content from a given real or fake video, facilitating the creation of
counterfeit multimodal material. The proposed method uses Text-to-Speech (TTS)
and Dynamic Time Warping techniques to achieve realistic speech tracks. Then,
we use the pipeline to generate and release TIMIT-TTS, a synthetic speech
dataset containing the most cutting-edge methods in the TTS field. This can be
used as a standalone audio dataset, or combined with other state-of-the-art
sets to perform multimodal research. Finally, we present numerous experiments
to benchmark the proposed dataset in both mono and multimodal conditions,
showing the need for multimodal forensic detectors and more suitable data
Use of Binary Cumulative Sums and Moving Averages in Nosocomial Infection Cluster Detection1
Clusters of nosocomial infection often occur undetected, at substantial cost to the medical system and individual patients. We evaluated binary cumulative sum (CUSUM) and moving average (MA) control charts for automated detection of nosocomial clusters. We selected two outbreaks with genotyped strains and used resistance as inputs to the control charts. We identified design parameters for the CUSUM and MA (window size, k, α, β, p0, p1) that detected both outbreaks, then calculated an associated positive predictive value (PPV) and time until detection (TUD) for sensitive charts. For CUSUM, optimal performance (high PPV, low TUD, fully sensitive) was for 0.1 <α ≤0.25 and 0.2 <β <0.25, with p0 = 0.05, with a mean TUD of 20 (range 8–43) isolates. Mean PPV was 96.5% (relaxed criteria) to 82.6% (strict criteria). MAs had a mean PPV of 88.5% (relaxed criteria) to 46.1% (strict criteria). CUSUM and MA may be useful techniques for automated surveillance of resistant infections
Stem Cell Therapy with Overexpressed VEGF and PDGF Genes Improves Cardiac Function in a Rat Infarct Model
Therapeutic potential was evaluated in a rat model of myocardial infarction using nanofiber-expanded human cord blood derived hematopoietic stem cells (CD133+/CD34+) genetically modified with VEGF plus PDGF genes (VIP).Myocardial function was monitored every two weeks up to six weeks after therapy. Echocardiography revealed time dependent improvement of left ventricular function evaluated by M-mode, fractional shortening, anterior wall tissue velocity, wall motion score index, strain and strain rate in animals treated with VEGF plus PDGF overexpressed stem cells (VIP) compared to nanofiber expanded cells (Exp), freshly isolated cells (FCB) or media control (Media). Improvement observed was as follows: VIP>Exp> FCB>media. Similar trend was noticed in the exercise capacity of rats on a treadmill. These findings correlated with significantly increased neovascularization in ischemic tissue and markedly reduced infarct area in animals in the VIP group. Stem cells in addition to their usual homing sites such as lung, spleen, bone marrow and liver, also migrated to sites of myocardial ischemia. The improvement of cardiac function correlated with expression of heart tissue connexin 43, a gap junctional protein, and heart tissue angiogenesis related protein molecules like VEGF, pNOS3, NOS2 and GSK3. There was no evidence of upregulation in the molecules of oncogenic potential in genetically modified or other stem cell therapy groups.Regenerative therapy using nanofiber-expanded hematopoietic stem cells with overexpression of VEGF and PDGF has a favorable impact on the improvement of rat myocardial function accompanied by upregulation of tissue connexin 43 and pro-angiogenic molecules after infarction
Azithromycin-chloroquine and the intermittent preventive treatment of malaria in pregnancy
In the high malaria-transmission settings of sub-Saharan Africa, malaria in pregnancy is an important cause of maternal, perinatal and neonatal morbidity. Intermittent preventive treatment of malaria in pregnancy (IPTp) with sulphadoxine-pyrimethamine (SP) reduces the incidence of low birth-weight, pre-term delivery, intrauterine growth-retardation and maternal anaemia. However, the public health benefits of IPTp are declining due to SP resistance. The combination of azithromycin and chloroquine is a potential alternative to SP for IPTp. This review summarizes key in vitro and in vivo evidence of azithromycin and chloroquine activity against Plasmodium falciparum and Plasmodium vivax, as well as the anticipated secondary benefits that may result from their combined use in IPTp, including the cure and prevention of many sexually transmitted diseases. Drug costs and the necessity for external financing are discussed along with a range of issues related to drug resistance and surveillance. Several scientific and programmatic questions of interest to policymakers and programme managers are also presented that would need to be addressed before azithromycin-chloroquine could be adopted for use in IPTp
Finishing the euchromatic sequence of the human genome
The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead
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