Biblio

In this paper, we propose a new method to expose AI-generated fake face images or videos (commonly known as the Deep Fakes). Our method is based on the observations that Deep Fakes are created by splicing synthesized face region into the original image, and in doing so, introducing errors that can be revealed when 3D head poses are estimated from the face images. We perform experiments to demonstrate this phenomenon and further develop a classification method based on this cue. Using features based on this cue, an SVM classifier is evaluated using a set of real face images and Deep Fakes.

Robots operating alongside humans in field environments have the potential to greatly increase the situational awareness of their human teammates. A significant challenge, however, is the efficient conveyance of what the robot perceives to the human in order to achieve improved situational awareness. We believe augmented reality (AR), which allows a human to simultaneously perceive the real world and digital information situated virtually in the real world, has the potential to address this issue. Motivated by the emerging prevalence of practical human-wearable AR devices, we present a system that enables a robot to perform cooperative search with a human teammate, where the robot can both share search results and assist the human teammate in navigation to the search target. We demonstrate this ability in a search task in an uninstrumented environment where the robot identifies and localizes targets and provides navigation direction via AR to bring the human to the correct target.

Neural Style Transfer based on convolutional neural networks has produced visually appealing results for image and video data in the recent years where e.g. the content of a photo and the style of a painting are merged to a novel piece of digital art. In practical engineering development, we utilize 3D objects as standard for optimizing digital shapes. Since these objects can be represented as binary 3D voxel representation, we propose to extend the Neural Style Transfer method to 3D geometries in analogy to 2D pixel representations. In a series of experiments, we first evaluate traditional Neural Style Transfer on 2D binary monochromatic images. We show that this method produces reasonable results on binary images lacking color information and even improve them by introducing a standardized Gram matrix based loss function for style. For an application of Neural Style Transfer on 3D voxel primitives, we trained several classifier networks demonstrating the importance of a meaningful convolutional network architecture. The standardization of the Gram matrix again strongly contributes to visually improved, less noisy results. We conclude that Neural Style Transfer extended by a standardization of the Gram matrix is a promising approach for generating novel 3D voxelized objects and expect future improvements with increasing graphics memory availability for finer object resolutions.

Digital-Twins simulate physical world objects by creating 'as-is' virtual images in a cyberspace. In order to create a well synchronized digital-twin simulator in manufacturing, information and activities of a physical machine need to be virtualized. Many existing digital-twins stream read-only data of machine sensors and do not incorporate operations of manufacturing machines through Internet. In this paper, a new method of virtualization is proposed to integrate machining data and operations into the digital-twins using Internet scale machine tool communication method. A fully functional digital-twin is implemented in CPMC testbed using MTComm and several manufacturing application scenarios are developed to evaluate the proposed method and system. Performance analysis shows that it is capable of providing data-driven visual monitoring of a manufacturing process and performing manufacturing operations through digital twins over the Internet. Results of the experiments also shows that the MTComm based digital twins have an excellent efficiency.

As 5G transitions from an industrial vision to a tangible, next-generation mobile technology, security remains key business driver. Heterogeneous environment, new networking paradigms and novel use cases makes 5G vulnerable to new security threats. This in turn necessitates a flexible and dependable security mechanism. End-to-End (E2E) data protection provides better security, avoids repeated security operations like encryption/decryption and provides differentiated security based on the services. E2E security deals with authentication, integrity, key management and confidentiality. The attack surface of a 5G system is larger as 5G aims for a heterogeneous networked society. Hence attack resistance needs to be a design consideration when defining new 5G protocols. This framework has been designed for accessing the manifold applications with high security and trust by offering E2E security for various services. The proposed framework is evaluated based on computation complexity, communication complexity, attack resistance rate and security defensive rate. The protocol is also evaluated for correctness, and resistance against passive, active and dictionary attacks using random oracle model and Automated Validation of Internet Security Protocols and Applications (AVISPA) tool.

Security is one of the more challenging problem for wireless Ad-Hoc networks specially in MANT due their features like dynamic topology, no centralized infrastructure, open architecture, etc. that make its more prone to different attacks. These attacks can be passive or active. The passive attack it hard to detect it in the network because its targets the confidential of data packet by eavesdropping on it. Therefore, the privacy preservation for data packets payload which it transmission over MANET has been a major part of concern. especially for safety-sensitive applications such as, privacy conference meetings, military applications, etc. In this paper it used symmetric cryptography to provide privacy for data packet by proposed modified AES based on five proposed which are: Key generation based on multi chaotic system, new SubByte, new ShiftRows, Add-two-XOR, Add-Shiftcycl.

We propose a novel visualization technique (Eagle-Eye) for intrusion detection, which visualizes a host as a commu- nity of system call traces in two-dimensional space. The goal of EagleEye is to visually cluster the system call traces. Although human eyes can easily perceive anomalies using EagleEye view, we propose two different methods called SAM and CPM that use the concept of data depth to help administrators distinguish between normal and abnormal behaviors. Our experimental results conducted on Australian Defence Force Academy Linux Dataset (ADFA-LD), which is a modern system calls dataset that includes new exploits and attacks on various programs, show EagleEye's efficiency in detecting diverse exploits and attacks.

The confinement of light in three dimensions (3D) is an active research topic in Nanophotonics, since it allows for ultimate control over photons [1]. A powerful tool to this end is a 3D photonic band gap crystal with a tailored defect that acts as a cavity or even a waveguide [2]. When a one-dimensional array of cavities is coupled, an intricate waveguiding system appears, known as a CROW (coupled resonator optical waveguide) [3]. Remarkably, 3D superlattices of coupled cavities that resonate inside a 3D band gap have not been studied to date. Recently, theoretical work has predicted the occurrence of "Cartesian light", wherein light propagates by hopping only in high symmetry directions in space [4]. This represents the optical analog of the Anderson model for spins or electrons that is relevant for neuromorphic computing and may lead to intricate lasing [5].

Most augmented reality applications connect virtual information to anchors, i.e. physical places or objects, by using spatial overlays or proximity. However, for industrial use cases this is not always feasible because specific parts must remain fully visible in order to meet work or security requirements. In these situations virtual information must be displayed at alternative positions while connections to anchors must still be clearly recognizable. In our previous research we were the first to show that for simple scenes connection lines are most suitable for this. To extend these results to more complex environments, we conducted an experiment on the effects of visual interruptions in connection lines and incorrect occlusion. Completion time and subjective mental effort for search tasks were used as measures. Our findings confirm that also in 3D scenes with partial occlusion connection lines are preferable to connect virtual information with anchors if an assignment via overlay or close proximity is not feasible. The results further imply that neither incorrectly used depth cues nor missing parts of connection lines make a significant difference concerning completion time or subjective mental effort. For designers of industrial augmented reality applications this means that they can choose either visualization based on their needs.

Biometric recognition systems based on ultrasound images have been investigated for several decades, and nowadays ultrasonic fingerprint sensors are fully integrated in portable devices. Main advantage of the Ultrasound over other technologies are the possibility to collect 3D images, allowing to gain information on under-skin features, which improve recognition accuracy and resistance to spoofing. Also, ultrasound images are not sensible to several skin contaminations, humidity and not uniform ambient illumination. An ultrasound system, able to acquire 3D images of the human palm has been recently proposed. In this work, a recognition procedure based on 2D palmprint images collected with this system is proposed and evaluated through verification experiments carried out on a home made database composed of 141 samples collected from 24 users. Perspective of the proposed method by upgrading the recognition procedure to provide a 3D template able to accounts for palm lines' depth are finally highlighted and discussed.

With the globalization of manufacturing and supply chains, ensuring the security and trustworthiness of ICs has become an urgent challenge. Split manufacturing (SM) and layout camouflaging (LC) are promising techniques to protect the intellectual property (IP) of ICs from malicious entities during and after manufacturing (i.e., from untrusted foundries and reverse-engineering by end-users). In this paper, we strive for “the best of both worlds,” that is of SM and LC. To do so, we extend both techniques towards 3D integration, an up-and-coming design and manufacturing paradigm based on stacking and interconnecting of multiple chips/dies/tiers. Initially, we review prior art and their limitations. We also put forward a novel, practical threat model of IP piracy which is in line with the business models of present-day design houses. Next, we discuss how 3D integration is a naturally strong match to combine SM and LC. We propose a security-driven CAD and manufacturing flow for face-to-face (F2F) 3D ICs, along with obfuscation of interconnects. Based on this CAD flow, we conduct comprehensive experiments on DRC-clean layouts. Strengthened by an extensive security analysis (also based on a novel attack to recover obfuscated F2F interconnects), we argue that entering the next, third dimension is eminent for effective and efficient IP protection.

Within “Industrie 4.0” approach 3D printing technology is characterized as one of the disruptive innovations. Conventional supply chains are replaced by value-added networks. The spatially distributed development of printed components, e.g. for the rapid delivery of spare parts, creates a new challenge when differentiating between “original part”, “copy” or “counterfeit” becomes necessary. This is especially true for safety-critical products. Based on these changes classic branded products adopt the characteristics of licensing models as we know them in the areas of software and digital media. This paper describes the use of digital rights management as a key technology for the successful transition to Additive Manufacturing methods and a key for its commercial implementation and the prevention of intellectual property theft. Risks will be identified along the process chain and solution concepts are presented. These are currently being developed by an 8-partner project named SAMPL (Secure Additive Manufacturing Platform).

Researchers develop bioassays by rigorously experimenting in the lab. This involves significant fiscal and skilled person-hour investment. A competitor can reverse engineer a bioassay implementation by imaging or taking a video of a biochip when in use. Thus, there is a need to protect the intellectual property (IP) rights of the bioassay developer. We introduce a novel 3D multilayer-based obfuscation to protect a biochip against reverse engineering.

Rapid inspection and assessment of critical infrastructure after man-made and natural disasters is a matter of homeland security. The primary aim of this paper is to demonstrate the potential of leveraging small Unmanned Aircraft System (sUAS) in support of the rapid recovery of critical infrastructure in the aftermath of catastrophic events. We propose our data collection, detection and assessment system, using a sUAS equipped with a Lidar and a camera. This method provides a solution in fast post-disaster response and assists human responders in damage investigation.

This paper explores the benefits of 3D face modeling for in-the-wild facial expression recognition (FER). Since there is limited in-the-wild 3D FER dataset, we first construct 3D facial data from available 2D dataset using recent advances in 3D face reconstruction. The 3D facial geometry representation is then extracted by deep learning technique. In addition, we also take advantage of manipulating the 3D face, such as using 2D projected images of 3D face as additional input for FER. These features are then fused with that of 2D FER typical network. By doing so, despite using common approaches, we achieve a competent recognition accuracy on Real-World Affective Faces (RAF) database and Static Facial Expressions in the Wild (SFEW 2.0) compared with the state-of-the-art reports. To the best of our knowledge, this is the first time such a deep learning combination of 3D and 2D facial modalities is presented in the context of in-the-wild FER.

To solve the high-resolution three-dimensional (3D) microwave imaging is a challenging topic due to its inherent unmanageable computation. Recently, deep learning techniques that can fully explore the prior of meaningful pattern embodied in data have begun to show its intriguing merits in various areas of inverse problem. Motivated by this observation, we here present a deep-learning-inspired approach to the high-resolution 3D microwave imaging in the context of Generative Adversarial Network (GAN), termed as GANMI in this work. Simulation and experimental results have been provided to demonstrate that the proposed GANMI can remarkably outperform conventional methods in terms of both the image quality and computational time.

The methods are based on injecting unpredictability into means and objects of protection are called stochastic methods of information security. The effective protection can be done only by using stochastic methods against an active opponent. The effectiveness of stochastic protection methods is defined by the quality of the used pseudo-random number generators and hash functions. The proposed hashing algorithm DOZENHASH is based on the using of 3D stochastic transformations of DOZEN family. The principal feature of the algorithm is that all input and output data blocks as well as intermediate results of calculations are represented as three-dimensional array of bytes with 4 bytes in each dimension. Thus, the resulting transformation has a high degree of parallelism at the level of elementary operations, in other words, it is focused on the implementation using heterogeneous supercomputer technologies.

This work presents the design and implementation of a large curved display system in a virtual reality (VR) environment that supports visualization of 2D datasets (e.g., images, buttons and text). By using this system, users are allowed to interact with data in front of a wide field of view and gain a high level of perceived immersion. We exhibit two use cases of this system, including (1) a virtual image wall as the display component of a 3D user interface, and (2) an inventory interface for a VR-based educational game. The use cases demonstrate capability and flexibility of curved displays in supporting varied purposes of data interaction within virtual environments.

Over the past few years, virtual and mixed reality systems have evolved significantly yielding high immersive experiences. Most of the metaphors used for interaction with the virtual environment do not provide the same meaningful feedback, to which the users are used to in the real world. This paper proposes a cyber-glove to improve the immersive sensation and the degree of embodiment in virtual and mixed reality interaction tasks. In particular, we are proposing a cyber-glove system that tracks wrist movements, hand orientation and finger movements. It provides a decoupled position of the wrist and hand, which can contribute to a better embodiment in interaction and manipulation tasks. Additionally, the detection of the curvature of the fingers aims to improve the proprioceptive perception of the grasping/releasing gestures more consistent to visual feedback. The cyber-glove system is being developed for VR applications related to real estate promotion, where users have to go through divisions of the house and interact with objects and furniture. This work aims to assess if glove-based systems can contribute to a higher sense of immersion, embodiment and usability when compared to standard VR hand controller devices (typically button-based). Twenty-two participants tested the cyber-glove system against the HTC Vive controller in a 3D manipulation task, specifically the opening of a virtual door. Metric results showed that 83% of the users performed faster door pushes, and described shorter paths with their hands wearing the cyber-glove. Subjective results showed that all participants rated the cyber-glove based interactions as equally or more natural, and 90% of users experienced an equal or a significant increase in the sense of embodiment.

Mixed reality (MR) technologies are widely used in distributed collaborative learning scenarios and have made learning and training more flexible and intuitive. However, there are many challenges in the use of MR due to the difficulty in creating a physical presence, particularly when a physical task is being performed collaboratively. We therefore developed a novel MR system to overcomes these limitations and enhance the distributed collaboration user experience. The primary objective of this paper is to explore the potential of a MR-based hand gestures system to enhance the conceptual architecture of MR in terms of both visualization and interaction in distributed collaboration. We propose a synchronous prototype named MRCollab as an immersive collaborative approach that allows two or more users to communicate with a peer based on the integration of several technologies such as video, audio, and hand gestures.

Security often requires that the data must be kept safe from unauthorized access. And the best line of speech communication is security. However, most computers are interconnected with each other openly, thereby exposing them and the communication channels that person uses. Speech cryptography secures information by protecting its confidentiality. It can also be used to protect information about the integrity and authenticity of data. Stronger cryptographic techniques are needed to ensure the integrity of data stored on a machine that may be infected or under attack. So far speech cryptography is used in many forms but using it with Audio file is another stronger technique. The process of cryptography happens with audio file for transferring more secure sensitive data. The audio file is encrypted and decrypted by using Lorenz 3D mapping and then 3D mapping function is converted into 2D mapping function by using euler's numerical resolution and strong algorithm provided by using henon mapping and then decrypted by using reverse of encryption. By implementing this, the resultant audio file will be in secured form.

Deterministic chaotic systems have been studied and developed in various fields of research. Dynamical systems with chaotic dynamics have different applications in communication, security and computation. Chaotic behaviors can be created by even simple nonlinear systems which can be implemented on low-cost hardware platforms. This paper presents a high-speed and low-cost hardware of three-dimensional chaotic flows without equilibrium. The proposed chaotic hardware is able to reproduce the main mechanism and dynamical behavior of the 3D chaotic flows observed in simulation, then a Chaotic Pseudo Random Number Generator is designed based on a 3D chaotic system. The proposed hardware is implemented with low computational overhead on an FPGA board, as a proof of concept. This low-cost chaotic hardware can be utilized in embedded and lightweight systems for a variety of chaotic based digital systems such as digital communication systems, and cryptography systems based on chaos theory for Security and IoT applications.

Data's security being important aspect of the today's internet is gaining more importance day by day. With the increase in online data exchange, transactions and payments; secure payment and secure data transfers have become an area of concern. Cryptography makes the data transmission over the internet secure by various methods, algorithms. Cryptography helps in avoiding the unauthorized people accessing the data by authentication, confidentiality, integrity and non-repudiation. In order to securely transmit the data many cryptographic algorithms are present, but the algorithm to be used should be robust, efficient, cost effective, high performance and easily deployable. Choosing an algorithm which suits the customer's requirement is an utmost important task. The proposed work discusses different symmetric key cryptographic algorithms like DES, 3DES, AES and Blowfish by considering encryption time, decryption time, entropy, memory usage, throughput, avalanche effect and energy consumption by practical implementation using java. Practical implementation of algorithms has been highlighted in proposed work considering tradeoff performance in terms of cost of various parameters rather than mere theoretical concepts. Battery consumption and avalanche effect of algorithms has been discussed. It reveals that AES performs very well in overall performance analysis among considered algorithms.

Starting from a common fictional scenario, simulated data sources and a set of measurements will feed two different visualization techniques with the aim to make a comparative analysis. Both visualization techniques described in this paper use the operational picture concept, deemed as the most appropriate tool for military commanders and their staff to achieve cyber situational awareness and to understand the cyber defence implications in operations. Cyber Common Operational Picture (CyCOP) is a tool developed by Universitat Politècnica de València in collaboration with the Spanish Ministry of Defence whose objective is to generate the Cyber Hybrid Situational Awareness (CyHSA). Royal Military Academy in Belgium developed a 3D Operational Picture able to display mission critical elements intuitively using a priori defined domain-knowledge. A comparative analysis will assist researchers in their way to progress solutions and implementation aspects.

Additive manufacturing (AM, or 3D printing) is a novel manufacturing technology that has been adopted in industrial and consumer settings. However, the reliance of this technology on computerization has raised various security concerns. In this paper, we address issues associated with sabotage via tampering during the 3D printing process by presenting an approach that can verify the integrity of a 3D printed object. Our approach operates on acoustic side-channel emanations generated by the 3D printer’s stepper motors, which results in a non-intrusive and real-time validation process that is difficult to compromise. The proposed approach constitutes two algorithms. The first algorithm is used to generate a master audio fingerprint for the verifiable unaltered printing process. The second algorithm is applied when the same 3D object is printed again, and this algorithm validates the monitored 3D printing process by assessing the similarity of its audio signature with the master audio fingerprint. To evaluate the quality of the proposed thresholds, we identify the detectability thresholds for the following minimal tampering primitives: insertion, deletion, replacement, and modification of a single tool path command. By detecting the deviation at the time of occurrence, we can stop the printing process for compromised objects, thus saving time and preventing material waste. We discuss various factors that impact the method, such as background noise, audio device changes and different audio recorder positions.