5,688 research outputs found
xLED: Covert Data Exfiltration from Air-Gapped Networks via Router LEDs
In this paper we show how attackers can covertly leak data (e.g., encryption
keys, passwords and files) from highly secure or air-gapped networks via the
row of status LEDs that exists in networking equipment such as LAN switches and
routers. Although it is known that some network equipment emanates optical
signals correlated with the information being processed by the device
('side-channel'), intentionally controlling the status LEDs to carry any type
of data ('covert-channel') has never studied before. A malicious code is
executed on the LAN switch or router, allowing full control of the status LEDs.
Sensitive data can be encoded and modulated over the blinking of the LEDs. The
generated signals can then be recorded by various types of remote cameras and
optical sensors. We provide the technical background on the internal
architecture of switches and routers (at both the hardware and software level)
which enables this type of attack. We also present amplitude and frequency
based modulation and encoding schemas, along with a simple transmission
protocol. We implement a prototype of an exfiltration malware and discuss its
design and implementation. We evaluate this method with a few routers and
different types of LEDs. In addition, we tested various receivers including
remote cameras, security cameras, smartphone cameras, and optical sensors, and
also discuss different detection and prevention countermeasures. Our experiment
shows that sensitive data can be covertly leaked via the status LEDs of
switches and routers at a bit rates of 10 bit/sec to more than 1Kbit/sec per
LED
Gaussian mixture model based probabilistic modeling of images for medical image segmentation
In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art. INDEX TERMS Gaussian Mixture Model, Level Sets, Active Contours, Biomedical Engineerin
Hidden and Uncontrolled - On the Emergence of Network Steganographic Threats
Network steganography is the art of hiding secret information within innocent
network transmissions. Recent findings indicate that novel malware is
increasingly using network steganography. Similarly, other malicious activities
can profit from network steganography, such as data leakage or the exchange of
pedophile data. This paper provides an introduction to network steganography
and highlights its potential application for harmful purposes. We discuss the
issues related to countering network steganography in practice and provide an
outlook on further research directions and problems.Comment: 11 page
ICface: Interpretable and Controllable Face Reenactment Using GANs
This paper presents a generic face animator that is able to control the pose
and expressions of a given face image. The animation is driven by human
interpretable control signals consisting of head pose angles and the Action
Unit (AU) values. The control information can be obtained from multiple sources
including external driving videos and manual controls. Due to the interpretable
nature of the driving signal, one can easily mix the information between
multiple sources (e.g. pose from one image and expression from another) and
apply selective post-production editing. The proposed face animator is
implemented as a two-stage neural network model that is learned in a
self-supervised manner using a large video collection. The proposed
Interpretable and Controllable face reenactment network (ICface) is compared to
the state-of-the-art neural network-based face animation techniques in multiple
tasks. The results indicate that ICface produces better visual quality while
being more versatile than most of the comparison methods. The introduced model
could provide a lightweight and easy to use tool for a multitude of advanced
image and video editing tasks.Comment: Accepted in WACV-202
Liver Segmentation from CT Images Using a Modified Distance Regularized Level Set Model Based on a Novel Balloon Force
Organ segmentation from medical images is still an open problem and liver segmentation is a much more challenging task among other organ segmentations. This paper presents a liver egmentation method from a sequence of computer tomography images.We propose a novel balloon force that controls the direction of the evolution process and slows down the evolving contour in regions with weak or without edges and discourages the evolving contour from going far away from the liver boundary or from leaking at a region that has a weak edge, or does not have an edge. The model is implemented using a modified Distance Regularized Level Set (DRLS) model. The experimental results show that the method can achieve a satisfactory result. Comparing with the original DRLS model, our model is more effective in dealing with over segmentation problems
CVABS: Moving Object Segmentation with Common Vector Approach for Videos
Background modelling is a fundamental step for several real-time computer
vision applications that requires security systems and monitoring. An accurate
background model helps detecting activity of moving objects in the video. In
this work, we have developed a new subspace based background modelling
algorithm using the concept of Common Vector Approach with Gram-Schmidt
orthogonalization. Once the background model that involves the common
characteristic of different views corresponding to the same scene is acquired,
a smart foreground detection and background updating procedure is applied based
on dynamic control parameters. A variety of experiments is conducted on
different problem types related to dynamic backgrounds. Several types of
metrics are utilized as objective measures and the obtained visual results are
judged subjectively. It was observed that the proposed method stands
successfully for all problem types reported on CDNet2014 dataset by updating
the background frames with a self-learning feedback mechanism.Comment: 12 Pages, 4 Figures, 1 Tabl
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