356 research outputs found
Inverse Design of Metamaterials for Tailored Linear and Nonlinear Optical Responses Using Deep Learning
The conventional process for developing an optimal design for nonlinear optical responses is based on a trial-and-error approach that is largely inefficient and does not necessarily lead to an ideal result. Deep learning can automate this process and widen the realm of nonlinear geometries and devices. This research illustrates a deep learning framework used to create an optimal plasmonic design for metamaterials with specific desired optical responses, both linear and nonlinear. The algorithm can produce plasmonic patterns that can maximize second-harmonic nonlinear effects of a nonlinear metamaterial. A nanolaminate metamaterial is used as a nonlinear material, and a plasmonic patterns are fabricated on the prepared nanolaminate to demonstrate the validity and efficacy of the deep learning algorithm for second-harmonic generation. Photonic upconversion from the infrared regime to the visible spectrum can occur through sum-frequency generation. The deep learning algorithm was improved to optimize a nonlinear plasmonic metamaterial for sum-frequency generation. The framework was then further expanded using transfer learning to lessen computation resources required to optimize metamaterials for new design parameters. The deep learning architecture applied in this research can be expanded to other optical responses and drive the innovation of novel optical applications.Ph.D
Covert Communication in Autoencoder Wireless Systems
The broadcast nature of wireless communications presents security and privacy challenges. Covert communication is a wireless security practice that focuses on intentionally hiding transmitted information. Recently, wireless systems have experienced significant growth, including the emergence of autoencoder-based models. These models, like other DNN architectures, are vulnerable to adversarial attacks, highlighting the need to study their susceptibility to covert communication. While there is ample research on covert communication in traditional wireless systems, the investigation of autoencoder wireless systems remains scarce. Furthermore, many existing covert methods are either detectable analytically or difficult to adapt to diverse wireless systems. The first part of this thesis provides a comprehensive examination of autoencoder-based communication systems in various scenarios and channel conditions. It begins with an introduction to autoencoder communication systems, followed by a detailed discussion of our own implementation and evaluation results. This serves as a solid foundation for the subsequent part of the thesis, where we propose a GAN-based covert communication model. By treating the covert sender, covert receiver, and observer as generator, decoder, and discriminator neural networks, respectively, we conduct joint training in an adversarial setting to develop a covert communication scheme that can be integrated into any normal autoencoder. Our proposal minimizes the impact on ongoing normal communication, addressing previous works shortcomings. We also introduce a training algorithm that allows for the desired tradeoff between covertness and reliability. Numerical results demonstrate the establishment of a reliable and undetectable channel between covert users, regardless of the cover signal or channel condition, with minimal disruption to the normal system operation
Privacy-preserving and Privacy-attacking Approaches for Speech and Audio -- A Survey
In contemporary society, voice-controlled devices, such as smartphones and
home assistants, have become pervasive due to their advanced capabilities and
functionality. The always-on nature of their microphones offers users the
convenience of readily accessing these devices. However, recent research and
events have revealed that such voice-controlled devices are prone to various
forms of malicious attacks, hence making it a growing concern for both users
and researchers to safeguard against such attacks. Despite the numerous studies
that have investigated adversarial attacks and privacy preservation for images,
a conclusive study of this nature has not been conducted for the audio domain.
Therefore, this paper aims to examine existing approaches for
privacy-preserving and privacy-attacking strategies for audio and speech. To
achieve this goal, we classify the attack and defense scenarios into several
categories and provide detailed analysis of each approach. We also interpret
the dissimilarities between the various approaches, highlight their
contributions, and examine their limitations. Our investigation reveals that
voice-controlled devices based on neural networks are inherently susceptible to
specific types of attacks. Although it is possible to enhance the robustness of
such models to certain forms of attack, more sophisticated approaches are
required to comprehensively safeguard user privacy
A review on visual privacy preservation techniques for active and assisted living
This paper reviews the state of the art in visual privacy protection techniques, with particular attention paid to techniques applicable to the field of Active and Assisted Living (AAL). A novel taxonomy with which state-of-the-art visual privacy protection methods can be classified is introduced. Perceptual obfuscation methods, a category in this taxonomy, is highlighted. These are a category of visual privacy preservation techniques, particularly relevant when considering scenarios that come under video-based AAL monitoring. Obfuscation against machine learning models is also explored. A high-level classification scheme of privacy by design, as defined by experts in privacy and data protection law, is connected to the proposed taxonomy of visual privacy preservation techniques. Finally, we note open questions that exist in the field and introduce the reader to some exciting avenues for future research in the area of visual privacy.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work is part of the visuAAL project on Privacy-Aware and Acceptable Video-Based Technologies and Services for Active and Assisted Living (https://www.visuaal-itn.eu/). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 861091. The authors would also like to acknowledge the contribution of COST Action CA19121 - GoodBrother, Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living (https://goodbrother.eu/), supported by COST (European Cooperation in Science and Technology) (https://www.cost.eu/)
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
Adversarial attacks and defenses in machine learning and deep neural network
have been gaining significant attention due to the rapidly growing applications
of deep learning in the Internet and relevant scenarios. This survey provides a
comprehensive overview of the recent advancements in the field of adversarial
attack and defense techniques, with a focus on deep neural network-based
classification models. Specifically, we conduct a comprehensive classification
of recent adversarial attack methods and state-of-the-art adversarial defense
techniques based on attack principles, and present them in visually appealing
tables and tree diagrams. This is based on a rigorous evaluation of the
existing works, including an analysis of their strengths and limitations. We
also categorize the methods into counter-attack detection and robustness
enhancement, with a specific focus on regularization-based methods for
enhancing robustness. New avenues of attack are also explored, including
search-based, decision-based, drop-based, and physical-world attacks, and a
hierarchical classification of the latest defense methods is provided,
highlighting the challenges of balancing training costs with performance,
maintaining clean accuracy, overcoming the effect of gradient masking, and
ensuring method transferability. At last, the lessons learned and open
challenges are summarized with future research opportunities recommended.Comment: 46 pages, 21 figure
A survey on artificial intelligence-based acoustic source identification
The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions
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
The Automation of the Extraction of Evidence masked by Steganographic Techniques in WAV and MP3 Audio Files
Antiforensics techniques and particularly steganography and cryptography have
become increasingly pressing issues that affect the current digital forensics
practice, both techniques are widely researched and developed as considered in
the heart of the modern digital era but remain double edged swords standing
between the privacy conscious and the criminally malicious, dependent on the
severity of the methods deployed. This paper advances the automation of hidden
evidence extraction in the context of audio files enabling the correlation
between unprocessed evidence artefacts and extreme Steganographic and
Cryptographic techniques using the Least Significant Bits extraction method
(LSB). The research generates an in-depth review of current digital forensic
toolkit and systems and formally address their capabilities in handling
steganography-related cases, we opted for experimental research methodology in
the form of quantitative analysis of the efficiency of detecting and extraction
of hidden artefacts in WAV and MP3 audio files by comparing standard industry
software. This work establishes an environment for the practical implementation
and testing of the proposed approach and the new toolkit for extracting
evidence hidden by Cryptographic and Steganographic techniques during forensics
investigations. The proposed multi-approach automation demonstrated a huge
positive impact in terms of efficiency and accuracy and notably on large audio
files (MP3 and WAV) which the forensics analysis is time-consuming and requires
significant computational resources and memory. However, the proposed
automation may occasionally produce false positives (detecting steganography
where none exists) or false negatives (failing to detect steganography that is
present) but overall achieve a balance between detecting hidden data accurately
along with minimising the false alarms.Comment: Wires Forensics Sciences Under Revie
Unsupervised Learning Algorithm for Noise Suppression and Speech Enhancement Applications
Smart and intelligent devices are being integrated more and more into day-to-day life to perform a multitude of tasks. These tasks include, but are not limited to, job automation, smart utility management, etc., with the aim to improve quality of life and to make normal day-to-day chores as effortless as possible. These smart devices may or may not be connected to the internet to accomplish tasks. Additionally, human-machine interaction with such devices may be touch-screen based or based on voice commands. To understand and act upon received voice commands, these devices require to enhance and distinguish the (clean) speech signal from the recorded noisy signal (that is contaminated by interference and background noise). The enhanced speech signal is then analyzed locally or in cloud to extract the command. This speech enhancement task may effectively be achieved if the number of recording microphones is large. But incorporating many microphones is only possible in large and expensive devices. With multiple microphones present, the computational complexity of speech enhancement algorithms is high, along with its power consumption requirements. However, if the device under consideration is small with limited power and computational capabilities, having multiple microphones is not possible. For example, hearing aids and cochlear implant devices. Thus, most of these devices have been developed with a single microphone. As a result of this handicap, developing a speech enhancement algorithm for assisted learning devices with a single microphone, while keeping computational complexity and power consumption of the said algorithm low, is a challenging problem. There has been considerable research to solve this problem with good speech enhancement performance. However, most real-time speech enhancement algorithms lose their effectiveness if the level of noise present in the recorded speech is high. This dissertation deals with this problem, i.e., the objective is to develop a method that enhances performance by reducing the input signal noise level. To this end, it is proposed to include a pre-processing step before applying speech enhancement algorithms. This pre-processing performs noise suppression in the transformed domain by generating an approximation of the noisy signals’ short-time Fourier transform. The approximated signal with improved input signal to noise ratio is then used by other speech enhancement algorithms to recover the underlying clean signal. This approximation is performed by using the proposed Block-Principal Component Analysis (Block-PCA) algorithm. To illustrate efficacy of the methodology, a detailed performance analysis under multiple noise types and noise levels is followed, which demonstrates that the inclusion of the pre-processing step improves considerably the performance of speech enhancement algorithms when compared to other approaches with no pre-processing steps
Content-Aware Quantization Index Modulation:Leveraging Data Statistics for Enhanced Image Watermarking
Image watermarking techniques have continuously evolved to address new
challenges and incorporate advanced features. The advent of data-driven
approaches has enabled the processing and analysis of large volumes of data,
extracting valuable insights and patterns. In this paper, we propose two
content-aware quantization index modulation (QIM) algorithms: Content-Aware QIM
(CA-QIM) and Content-Aware Minimum Distortion QIM (CAMD-QIM). These algorithms
aim to improve the embedding distortion of QIM-based watermarking schemes by
considering the statistics of the cover signal vectors and messages. CA-QIM
introduces a canonical labeling approach, where the closest coset to each cover
vector is determined during the embedding process. An adjacency matrix is
constructed to capture the relationships between the cover vectors and
messages. CAMD-QIM extends the concept of minimum distortion (MD) principle to
content-aware QIM. Instead of quantizing the carriers to lattice points,
CAMD-QIM quantizes them to close points in the correct decoding region.
Canonical labeling is also employed in CAMD-QIM to enhance its performance.
Simulation results demonstrate the effectiveness of CA-QIM and CAMD-QIM in
reducing embedding distortion compared to traditional QIM. The combination of
canonical labeling and the minimum distortion principle proves to be powerful,
minimizing the need for changes to most cover vectors/carriers. These
content-aware QIM algorithms provide improved performance and robustness for
watermarking applications.Comment: 12 pages, 10 figure
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