11 research outputs found
MĂ©thodes de tatouage robuste pour la protection de l imagerie numerique 3D
La multiplication des contenus stĂ©rĂ©oscopique augmente les risques de piratage numĂ©rique. La solution technologique par tatouage relĂšve ce dĂ©fi. En pratique, le dĂ©fi d une approche de tatouage est d'atteindre l Ă©quilibre fonctionnel entre la transparence, la robustesse, la quantitĂ© d information insĂ©rĂ©e et le coĂ»t de calcul. Tandis que la capture et l'affichage du contenu 3D ne sont fondĂ©es que sur les deux vues gauche/droite, des reprĂ©sentations alternatives, comme les cartes de disparitĂ© devrait Ă©galement ĂȘtre envisagĂ©e lors de la transmission/stockage. Une Ă©tude spĂ©cifique sur le domaine d insertion optimale devient alors nĂ©cessaire. Cette thĂšse aborde les dĂ©fis mentionnĂ©s ci-dessus. Tout d'abord, une nouvelle carte de disparitĂ© (3D video-New Three Step Search- 3DV-SNSL) est dĂ©veloppĂ©e. Les performances des 3DV-NTSS ont Ă©tĂ© Ă©valuĂ©es en termes de qualitĂ© visuelle de l'image reconstruite et coĂ»t de calcul. En comparaison avec l'Ă©tat de l'art (NTSS et FS-MPEG) des gains moyens de 2dB en PSNR et 0,1 en SSIM sont obtenus. Le coĂ»t de calcul est rĂ©duit par un facteur moyen entre 1,3 et 13. DeuxiĂšmement, une Ă©tude comparative sur les principales classes hĂ©ritĂ©es des mĂ©thodes de tatouage 2D et de leurs domaines d'insertion optimales connexes est effectuĂ©e. Quatre mĂ©thodes d'insertion appartenant aux familles SS, SI et hybride (Fast-IProtect) sont considĂ©rĂ©es. Les expĂ©riences ont mis en Ă©vidence que Fast-IProtect effectuĂ© dans la nouvelle carte de disparitĂ© (3DV-NTSS) serait suffisamment gĂ©nĂ©rique afin de servir une grande variĂ©tĂ© d'applications. La pertinence statistique des rĂ©sultats est donnĂ©e par les limites de confiance de 95% et leurs erreurs relatives infĂ©rieurs er <0.1The explosion in stereoscopic video distribution increases the concerns over its copyright protection. Watermarking can be considered as the most flexible property right protection technology. The watermarking applicative issue is to reach the trade-off between the properties of transparency, robustness, data payload and computational cost. While the capturing and displaying of the 3D content are solely based on the two left/right views, some alternative representations, like the disparity maps should also be considered during transmission/storage. A specific study on the optimal (with respect to the above-mentioned properties) insertion domain is also required. The present thesis tackles the above-mentioned challenges. First, a new disparity map (3D video-New Three Step Search - 3DV-NTSS) is designed. The performances of the 3DV-NTSS were evaluated in terms of visual quality of the reconstructed image and computational cost. When compared with state of the art methods (NTSS and FS-MPEG) average gains of 2dB in PSNR and 0.1 in SSIM are obtained. The computational cost is reduced by average factors between 1.3 and 13. Second, a comparative study on the main classes of 2D inherited watermarking methods and on their related optimal insertion domains is carried out. Four insertion methods are considered; they belong to the SS, SI and hybrid (Fast-IProtect) families. The experiments brought to light that the Fast-IProtect performed in the new disparity map domain (3DV-NTSS) would be generic enough so as to serve a large variety of applications. The statistical relevance of the results is given by the 95% confidence limits and their underlying relative errors lower than er<0.1EVRY-INT (912282302) / SudocSudocFranceF
Applied Metaheuristic Computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics
This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ⌠7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p
Medical image integrity control and forensics based on watermarking -- Approximating local modifications and identifying global image alterations
International audienceIn this paper we present a medical image integrity verification system that not only allows detecting and approximating malevolent local image alterations (e.g. removal or addition of findings) but is also capable to identify the nature of global image processing applied to the image (e.g. lossy compression, filtering ...). For that purpose, we propose an image signature derived from the geometric moments of pixel blocks. Such a signature is computed over regions of interest of the image and then watermarked in regions of non interest. Image integrity analysis is conducted by comparing embedded and recomputed signatures. If any, local modifications are approximated through the determination of the parameters of the nearest generalized 2D Gaussian. Image moments are taken as image features and serve as inputs to one classifier we learned to discriminate the type of global image processing. Experimental results with both local and global modifications illustrate the overall performances of our approach
An examination of the Asus WL-HDD 2.5 as a nepenthes malware collector
The Linksys WRT54g has been used as a host for network forensics tools for instance Snort for a long period of time. Whilst large corporations are already utilising network forensic tools, this paper demonstrates that it is quite feasible for a non-security specialist to track and capture malicious network traffic. This paper introduces the Asus Wireless Hard disk as a replacement for the popular Linksys WRT54g. Firstly, the Linksys router will be introduced detailing some of the research that was undertaken on the device over the years amongst the security community. It then briefly discusses malicious software and the impact this may have for a home user. The paper then outlines the trivial steps in setting up Nepenthes 0.1.7 (a malware collector) for the Asus WL-HDD 2.5 according to the Nepenthes and tests the feasibility of running the malware collector on the selected device. The paper then concludes on discussing the limitations of the device when attempting to execute Nepenthes
Dynamical Systems
Complex systems are pervasive in many areas of science integrated in our daily lives. Examples include financial markets, highway transportation networks, telecommunication networks, world and country economies, social networks, immunological systems, living organisms, computational systems and electrical and mechanical structures. Complex systems are often composed of a large number of interconnected and interacting entities, exhibiting much richer global scale dynamics than the properties and behavior of individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. This special issue therefore intends to contribute towards the dissemination of the multifaceted concepts in accepted use by the scientific community. We hope readers enjoy this pertinent selection of papers which represents relevant examples of the state of the art in present day research. [...
Applied Methuerstic computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
Towards Robust Deep Neural Networks
Deep neural networks (DNNs) enable state-of-the-art performance for most machine
learning tasks. Unfortunately, they are vulnerable to attacks, such as Trojans during
training and Adversarial Examples at test time. Adversarial Examples are inputs
with carefully crafted perturbations added to benign samples. In the Computer
Vision domain, while the perturbations being imperceptible to humans, Adversarial
Examples can successfully misguide or fool DNNs. Meanwhile, Trojan or backdoor
attacks involve attackers tampering with the training process, for example, to inject
poisoned training data to embed a backdoor into the network that can be activated
during model deployment when the Trojan triggers (known only to the attackers)
appear in the modelâs inputs. This dissertation investigates methods of building robust
DNNs against these training-time and test-time threats.
Recognising the threat of Adversarial Examples in the malware domain, this research
considers the problem of realising a robust DNN-based malware detector against Adversarial
Example attacks by developing a Bayesian adversarial learning algorithm. In contrast
to vision tasks, adversarial learning in a domain without a differentiable or invertible
mapping function from the problemspace (such as software code inputs) to the feature
space is hard. The study proposes an alternative; performing adversarial learning in
the feature space and proving the projection of perturbed yet, valid malware, in the
problem space into the feature space will be a subset of feature-space adversarial
attacks. The Bayesian approach improves benign performance, provably bounds
the difference between adversarial risk and empirical risk and improves robustness
against increasingly large attack budgets not employed during training.
To investigate the problem of improving the robustness of DNNs against Adversarial
Examplesâcarefully crafted perturbation added to inputsâin the Computer Vision
domain, the research considers the problem of developing a Bayesian learning algorithm to
realise a robust DNN against Adversarial Examples in the CV domain. Accordingly, a novel
Bayesian learning method is designed that conceptualises an information gain objective
to measure and force the information learned from both benign and Adversarial
Examples to be similar. This method proves that minimising this information gain
objective further tightens the bound of the difference between adversarial risk and empirical risk to move towards a basis for a principled method of adversarially training
BNNs.
Recognising the threat from backdoor or Trojan attacks against DNNs, the research
considers the problem of finding a robust defence method that is effective against Trojan
attacks. The research explores a new idea in the domain; sanitisation of inputs and
proposes Februus to neutralise highly potent and insidious Trojan attacks on DNN
systems at run-time. In Trojan attacks, an adversary activates a backdoor crafted in
a deep neural network model using a secret trigger, a Trojan, applied to any input
to alter the modelâs decision to a target predictionâa target determined by and only
known to the attacker. Februus sanitises the incoming input by surgically removing the
potential trigger artifacts and restoring the input for the classification task. Februus
enables effective Trojan mitigation by sanitising inputs with no loss of performance
for sanitised inputs, trojaned or benign. This method is highly effective at defending
against advanced Trojan attack variants as well as challenging, adaptive attacks where
attackers have full knowledge of the defence method.
Investigating the connections between Trojan attacks and spatially constrained
Adversarial Examples or so-called Adversarial Patches in the input space, the research
exposes an emerging threat; an attack exploiting the vulnerability of a DNN to generate
naturalistic adversarial patches as universal triggers. For the first time, a method based
on Generative Adversarial Networks is developed to exploit a GANâs latent space to
search for universal naturalistic adversarial patches. The proposed attackâs advantage
is its ability to exert a high level of control, enabling attackers to craft naturalistic
adversarial patches that are highly effective, robust against state-of-the-art DNNs, and
deployable in the physical world without needing to interfere with the model building
process or risking discovery. Until now, this has only been demonstrably possible
using Trojan attack methods.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202