607 research outputs found
スマート農業のための信頼できるドキュメンテーションシステム
九州工業大学博士学位論文 学位記番号:情工博甲第373号 学位授与年月日:令和4年12月27日1 Introduction|2 Traditional Documentation|3 Blockchain-based Trust Management|4 Adopting Blockchain Method for Cocoa Farming Documentation|5 Implementation of Blockchain Concept into the Real Problem through a Simulation Case of Cocoa Production|6 Conclusion and Future Work九州工業大学令和4年
Machine Learning Algorithm for the Scansion of Old Saxon Poetry
Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools
deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We
implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon
and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and
we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm
reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested
the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that
the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input
verses
Medical image encryption techniques: a technical survey and potential challenges
Among the most sensitive and important data in telemedicine systems are medical images. It is necessary to use a robust encryption method that is resistant to cryptographic assaults while transferring medical images over the internet. Confidentiality is the most crucial of the three security goals for protecting information systems, along with availability, integrity, and compliance. Encryption and watermarking of medical images address problems with confidentiality and integrity in telemedicine applications. The need to prioritize security issues in telemedicine applications makes the choice of a trustworthy and efficient strategy or framework all the more crucial. The paper examines various security issues and cutting-edge methods to secure medical images for use with telemedicine systems
A dual watermarking scheme for identity protection
A novel dual watermarking scheme with potential applications in identity protection, media integrity maintenance and copyright protection in both electronic and printed media is presented. The proposed watermarking scheme uses the owner’s signature and fingerprint as watermarks through which the ownership and validity of the media can be proven and kept intact. To begin with, the proposed watermarking scheme is implemented on continuous-tone/greyscale images, and later extended to images achieved via multitoning, an advanced version of halftoning-based printing. The proposed watermark embedding is robust and imperceptible. Experimental simulations and evaluations of the proposed method show excellent results from both objective and subjective view-points
Comparative Analysis of Techniques Used to Detect Copy-Move Tampering for Real-World Electronic Images
Evolution of high computational powerful computers, easy availability of several innovative editing software package and high-definition quality-based image capturing tools follows to effortless result in producing image forgery. Though, threats for security and misinterpretation of digital images and scenes have been observed to be happened since a long period and also a lot of research has been established in developing diverse techniques to authenticate the digital images. On the contrary, the research in this region is not limited to checking the validity of digital photos but also to exploring the specific signs of distortion or forgery. This analysis would not require additional prior information of intrinsic content of corresponding digital image or prior embedding of watermarks. In this paper, recent growth in the area of digital image tampering identification have been discussed along with benchmarking study has been shown with qualitative and quantitative results. With variety of methodologies and concepts, different applications of forgery detection have been discussed with corresponding outcomes especially using machine and deep learning methods in order to develop efficient automated forgery detection system. The future applications and development of advanced soft-computing based techniques in digital image forgery tampering has been discussed
An ensemble architecture for forgery detection and localization in digital images
Questa tesi presenta un approccio d'insieme unificato - "ensemble" - per il rilevamento e la localizzazione di contraffazioni in immagini digitali. Il focus della ricerca è su due delle più comuni ma efficaci tecniche di contraffazione: "copy-move" e "splicing". L'architettura proposta combina una serie di metodi di rilevamento e localizzazione di manipolazioni per ottenere prestazioni migliori rispetto a metodi utilizzati in modalità "standalone". I principali contributi di questo lavoro sono elencati di seguito.
In primo luogo, nel Capitolo 1 e 2 viene presentata un'ampia rassegna dell'attuale stato dell'arte nel rilevamento di manipolazioni ("forgery"), con particolare attenzione agli approcci basati sul deep learning. Un'importante intuizione che ne deriva è la seguente: questi approcci, sebbene promettenti, non possono essere facilmente confrontati in termini di performance perché tipicamente vengono valutati su dataset personalizzati a causa della mancanza di dati annotati con precisione. Inoltre, spesso questi dati non sono resi disponibili pubblicamente.
Abbiamo poi progettato un algoritmo di rilevamento di manipolazioni copy-move basato su "keypoint", descritto nel capitolo 3. Rispetto a esistenti approcci simili, abbiamo aggiunto una fase di clustering basato su densità spaziale per filtrare le corrispondenze rumorose dei keypoint. I risultati hanno dimostrato che questo metodo funziona bene su due dataset di riferimento e supera uno dei metodi più citati in letteratura.
Nel Capitolo 4 viene proposta una nuova architettura per predire la direzione della luce 3D in una data immagine. Questo approccio sfrutta l'idea di combinare un metodo "data-driven" con un modello di illuminazione fisica, consentendo così di ottenere prestazioni migliori. Al fine di sopperire al problema della scarsità di dati per l'addestramento di architetture di deep learning altamente parametrizzate, in particolare per il compito di scomposizione intrinseca delle immagini, abbiamo sviluppato due algoritmi di generazione dei dati. Questi sono stati utilizzati per produrre due dataset - uno sintetico e uno di immagini reali - con lo scopo di addestrare e valutare il nostro approccio.
Il modello di stima della direzione della luce proposto è stato sfruttato in un nuovo approccio di rilevamento di manipolazioni di tipo splicing, discusso nel Capitolo 5, in cui le incoerenze nella direzione della luce tra le diverse regioni dell'immagine vengono utilizzate per evidenziare potenziali attacchi splicing.
L'approccio ensemble proposto è descritto nell'ultimo capitolo. Questo include un modulo "FusionForgery" che combina gli output dei metodi "base" proposti in precedenza e assegna un'etichetta binaria (forged vs. original). Nel caso l'immagine sia identificata come contraffatta, il nostro metodo cerca anche di specializzare ulteriormente la decisione tra attacchi splicing o copy-move. In questo secondo caso, viene eseguito anche un tentativo di ricostruire le regioni "sorgente" utilizzate nell'attacco copy-move. Le prestazioni dell'approccio proposto sono state valutate addestrandolo e testandolo su un dataset sintetico, generato da noi, comprendente sia attacchi copy-move che di tipo splicing. L'approccio ensemble supera tutti i singoli metodi "base" in termini di prestazioni, dimostrando la validità della strategia proposta.This thesis presents a unified ensemble approach for forgery detection and localization in digital images. The focus of the research is on two of the most common but effective forgery techniques: copy-move and splicing. The ensemble architecture combines a set of forgery detection and localization methods in order to achieve improved performance with respect to standalone approaches. The main contributions of this work are listed in the following.
First, an extensive review of the current state of the art in forgery detection, with a focus on deep learning-based approaches is presented in Chapter 1 and 2. An important insight that is derived is the following: these approaches, although promising, cannot be easily compared in terms of performance because they are typically evaluated on custom datasets due to the lack of precisely annotated data. Also, they are often not publicly available.
We then designed a keypoint-based copy-move detection algorithm, which is described in Chapter 3. Compared to previous existing keypoints-based approaches, we added a density-based clustering step to filter out noisy keypoints matches. This method has been demonstrated to perform well on two benchmark datasets and outperforms one of the most cited state-of-the-art methods.
In Chapter 4 a novel architecture is proposed to predict the 3D light direction of the light in a given image. This approach leverages the idea of combining, in a data-driven method, a physical illumination model that allows for improved regression performance. In order to fill in the gap of data scarcity for training highly-parameterized deep learning architectures, especially for the task of intrinsic image decomposition, we developed two data generation algorithms that were used to produce two datasets - one synthetic and one of real images - to train and evaluate our approach.
The proposed light direction estimation model has then been employed to design a novel splicing detection approach, discussed in Chapter 5, in which light direction inconsistencies between different regions in the image are used to highlight potential splicing attacks.
The proposed ensemble scheme for forgery detection is described in the last chapter. It includes a "FusionForgery" module that combines the outputs of the different previously proposed "base" methods and assigns a binary label (forged vs. pristine) to the input image. In the case of forgery prediction, our method also tries to further specialize the decision between splicing and copy-move attacks. If the image is predicted as copy-moved, an attempt to reconstruct the source regions used in the copy-move attack is also done. The performance of the proposed approach has been assessed by training and testing it on a synthetic dataset, generated by us, comprising both copy-move and splicing attacks. The ensemble approach outperforms all of the individual "base" methods, demonstrating the validity of the proposed strategy
Robust image steganography method suited for prining = Robustna steganografska metoda prilagođena procesu tiska
U ovoj doktorskoj dizertaciji prezentirana je robustna steganografska metoda razvijena i
prilagođena za tisak. Osnovni cilj metode je pružanje zaštite od krivotvorenja ambalaže.
Zaštita ambalaže postiže se umetanjem više bitova informacije u sliku pri enkoderu, a potom
maskiranjem informacije kako bi ona bila nevidljiva ljudskom oku. Informacija se pri
dekoderu detektira pomoću infracrvene kamere. Preliminarna istraživanja pokazala su da u
relevantnoj literaturi nedostaje metoda razvijenih za domenu tiska. Razlog za takav
nedostatak jest činjenica da razvijanje steganografskih metoda za tisak zahtjeva veću količinu
resursa i materijala, u odnosu na razvijanje sličnih domena za digitalnu domenu. Također,
metode za tisak često zahtijevaju višu razinu kompleksnosti, budući da se tijekom
reprodukcije pojavljuju razni oblici procesiranja koji mogu kompromitirati informaciju u slici
[1]. Da bi se sačuvala skrivena informacija, metoda mora biti otporna na procesiranje koje se
događa tijekom reprodukcije.
Kako bi se postigla visoka razina otpornosti, informacija se može umetnuti unutar
frekvencijske domene slike [2], [3]. Frekvencijskoj domeni slike možemo pristupiti pomoću
matematičkih transformacija. Najčešće se koriste diskretna kosinusna transformacija (DCT),
diskretna wavelet transformacija (DWT) i diskretna Fourierova transformacija (DFT) [2], [4].
Korištenje svake od navedenih transformacija ima određene prednosti i nedostatke, ovisno o
kontekstu razvijanja metode [5]. Za metode prilagođene procesu tiska, diskretna Fourierova
transformacija je optimalan odabir, budući da metode bazirane na DFT-u pružaju otpornost
na geometrijske transformacije koje se događaju tijekom reprodukcije [5], [6].
U ovom istraživanju korištene su slike u cmyk prostoru boja. Svaka slika najprije je
podijeljena u blokove, a umetanje informacije vrši se za svaki blok pojedinačno. Pomoću
DFT-a, ???? kanal slikovnog bloka se transformira u frekvencijsku domenu, gdje se vrši
umetanje informacije. Akromatska zamjena koristi se za maskiranje vidljivih artefakata
nastalih prilikom umetanja informacije. Primjeri uspješnog korištenja akromatske zamjene za
maskiranje artefakata mogu se pronaći u [7] i [8]. Nakon umetanja informacije u svaki
slikovni blok, blokovi se ponovno spajaju u jednu, jedinstvenu sliku. Akromatska zamjena
tada mijenja vrijednosti c, m i y kanala slike, dok kanal k, u kojemu se nalazi umetnuta
informacija, ostaje nepromijenjen. Time nakon maskiranja akromatskom zamjenom označena
slika posjeduje ista vizualna svojstva kao i slika prije označavanja. U eksperimentalnom dijelu rada koristi se 1000 slika u cmyk prostoru boja. U digitalnom
okruženju provedeno je istraživanje otpornosti metode na slikovne napade specifične za
reprodukcijski proces - skaliranje, blur, šum, rotaciju i kompresiju. Također, provedeno je
istraživanje otpornosti metode na reprodukcijski proces, koristeći tiskane uzorke. Objektivna
metrika bit error rate (BER) korištena je za evaluaciju. Mogućnost optimizacije metode
testirala se procesiranjem slike (unsharp filter) i korištenjem error correction kodova (ECC).
Provedeno je istraživanje kvalitete slike nakon umetanja informacije. Za evaluaciju su
korištene objektivne metrike peak signal to noise ratio (PSNR) i structural similarity index
measure (SSIM). PSNR i SSIM su tzv. full-reference metrike. Drugim riječima, potrebne su i
neoznačena i označena slika istovremeno, kako bi se mogla utvrditi razina sličnosti između
slika [9], [10]. Subjektivna analiza provedena je na 36 ispitanika, koristeći ukupno 144
uzorka slika. Ispitanici su ocijenjivali vidljivost artefakata na skali od nula (nevidljivo) do tri
(vrlo vidljivo).
Rezultati pokazuju da metoda posjeduje visoku razinu otpornosti na reprodukcijski proces.
Također, metoda se uistinu optimizirala korištenjem unsharp filtera i ECC-a. Kvaliteta slike
ostaje visoka bez obzira na umetanje informacije, što su potvrdili rezultati eksperimenata s
objektivnim metrikama i subjektivna analiza
Security and Privacy on Generative Data in AIGC: A Survey
The advent of artificial intelligence-generated content (AIGC) represents a
pivotal moment in the evolution of information technology. With AIGC, it can be
effortless to generate high-quality data that is challenging for the public to
distinguish. Nevertheless, the proliferation of generative data across
cyberspace brings security and privacy issues, including privacy leakages of
individuals and media forgery for fraudulent purposes. Consequently, both
academia and industry begin to emphasize the trustworthiness of generative
data, successively providing a series of countermeasures for security and
privacy. In this survey, we systematically review the security and privacy on
generative data in AIGC, particularly for the first time analyzing them from
the perspective of information security properties. Specifically, we reveal the
successful experiences of state-of-the-art countermeasures in terms of the
foundational properties of privacy, controllability, authenticity, and
compliance, respectively. Finally, we summarize the open challenges and
potential exploration directions from each of theses properties
Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
Ensuring alignment, which refers to making models behave in accordance with
human intentions [1,2], has become a critical task before deploying large
language models (LLMs) in real-world applications. For instance, OpenAI devoted
six months to iteratively aligning GPT-4 before its release [3]. However, a
major challenge faced by practitioners is the lack of clear guidance on
evaluating whether LLM outputs align with social norms, values, and
regulations. This obstacle hinders systematic iteration and deployment of LLMs.
To address this issue, this paper presents a comprehensive survey of key
dimensions that are crucial to consider when assessing LLM trustworthiness. The
survey covers seven major categories of LLM trustworthiness: reliability,
safety, fairness, resistance to misuse, explainability and reasoning, adherence
to social norms, and robustness. Each major category is further divided into
several sub-categories, resulting in a total of 29 sub-categories.
Additionally, a subset of 8 sub-categories is selected for further
investigation, where corresponding measurement studies are designed and
conducted on several widely-used LLMs. The measurement results indicate that,
in general, more aligned models tend to perform better in terms of overall
trustworthiness. However, the effectiveness of alignment varies across the
different trustworthiness categories considered. This highlights the importance
of conducting more fine-grained analyses, testing, and making continuous
improvements on LLM alignment. By shedding light on these key dimensions of LLM
trustworthiness, this paper aims to provide valuable insights and guidance to
practitioners in the field. Understanding and addressing these concerns will be
crucial in achieving reliable and ethically sound deployment of LLMs in various
applications
The Journal of Conventional Weapons Destruction Issue 27.2
Updates on recent enhancements to IMAS. Food security and its connection to mine action as it applies to Ukraine. Digital EORE as a small NGO in mine action. A case study on moving beyond do no harm in environmental mainstreaming in mine action. Efforts of JICA and CMAC in fostering South-South cooperation in mine action. UAV Lidar imaging in mine action to detect and map minefields in Angola. Land disputes and rights in mine action. Computer vision detection of explosive ordnance
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