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2021-01-15
Đorđević, M., Milivojević, M., Gavrovska, A..  2019.  DeepFake Video Analysis using SIFT Features. 2019 27th Telecommunications Forum (℡FOR). :1—4.
Recent advantages in changing faces using DeepFake algorithms, which replace a face of one person with a face of another, truly represent what artificial intelligence and deep learning are capable of. Deepfakes in still images or video clips represent forgeries and tampered visual information. They are becoming increasingly successful and even difficult to notice in some cases. In this paper we analyze deepfakes using SIFT (Scale-Invariant Feature Transform) features. The experimental results show that in deepfake analysis using SIFT keypoints can be considered valuable.
Kumar, A., Bhavsar, A., Verma, R..  2020.  Detecting Deepfakes with Metric Learning. 2020 8th International Workshop on Biometrics and Forensics (IWBF). :1—6.
With the arrival of several face-swapping applications such as FaceApp, SnapChat, MixBooth, FaceBlender and many more, the authenticity of digital media content is hanging on a very loose thread. On social media platforms, videos are widely circulated often at a high compression factor. In this work, we analyze several deep learning approaches in the context of deepfakes classification in high compression scenarios and demonstrate that a proposed approach based on metric learning can be very effective in performing such a classification. Using less number of frames per video to assess its realism, the metric learning approach using a triplet network architecture proves to be fruitful. It learns to enhance the feature space distance between the cluster of real and fake videos embedding vectors. We validated our approaches on two datasets to analyze the behavior in different environments. We achieved a state-of-the-art AUC score of 99.2% on the Celeb-DF dataset and accuracy of 90.71% on a highly compressed Neural Texture dataset. Our approach is especially helpful on social media platforms where data compression is inevitable.
Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S..  2020.  Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). :3204—3213.
AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information. The need to develop and evaluate DeepFake detection algorithms calls for datasets of DeepFake videos. However, current DeepFake datasets suffer from low visual quality and do not resemble DeepFake videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5,639 high-quality DeepFake videos of celebrities generated using improved synthesis process. We conduct a comprehensive evaluation of DeepFake detection methods and datasets to demonstrate the escalated level of challenges posed by Celeb-DF.
Katarya, R., Lal, A..  2020.  A Study on Combating Emerging Threat of Deepfake Weaponization. 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). :485—490.
A breakthrough in the emerging use of machine learning and deep learning is the concept of autoencoders and GAN (Generative Adversarial Networks), architectures that can generate believable synthetic content called deepfakes. The threat lies when these low-tech doctored images, videos, and audios blur the line between fake and genuine content and are used as weapons to cause damage to an unprecedented degree. This paper presents a survey of the underlying technology of deepfakes and methods proposed for their detection. Based on a detailed study of all the proposed models of detection, this paper presents SSTNet as the best model to date, that uses spatial, temporal, and steganalysis for detection. The threat posed by document and signature forgery, which is yet to be explored by researchers, has also been highlighted in this paper. This paper concludes with the discussion of research directions in this field and the development of more robust techniques to deal with the increasing threats surrounding deepfake technology.
Khalid, H., Woo, S. S..  2020.  OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). :2794—2803.
An image forgery method called Deepfakes can cause security and privacy issues by changing the identity of a person in a photo through the replacement of his/her face with a computer-generated image or another person's face. Therefore, a new challenge of detecting Deepfakes arises to protect individuals from potential misuses. Many researchers have proposed various binary-classification based detection approaches to detect deepfakes. However, binary-classification based methods generally require a large amount of both real and fake face images for training, and it is challenging to collect sufficient fake images data in advance. Besides, when new deepfakes generation methods are introduced, little deepfakes data will be available, and the detection performance may be mediocre. To overcome these data scarcity limitations, we formulate deepfakes detection as a one-class anomaly detection problem. We propose OC-FakeDect, which uses a one-class Variational Autoencoder (VAE) to train only on real face images and detects non-real images such as deepfakes by treating them as anomalies. Our preliminary result shows that our one class-based approach can be promising when detecting Deepfakes, achieving a 97.5% accuracy on the NeuralTextures data of the well-known FaceForensics++ benchmark dataset without using any fake images for the training process.
Gandhi, A., Jain, S..  2020.  Adversarial Perturbations Fool Deepfake Detectors. 2020 International Joint Conference on Neural Networks (IJCNN). :1—8.
This work uses adversarial perturbations to enhance deepfake images and fool common deepfake detectors. We created adversarial perturbations using the Fast Gradient Sign Method and the Carlini and Wagner L2 norm attack in both blackbox and whitebox settings. Detectors achieved over 95% accuracy on unperturbed deepfakes, but less than 27% accuracy on perturbed deepfakes. We also explore two improvements to deep-fake detectors: (i) Lipschitz regularization, and (ii) Deep Image Prior (DIP). Lipschitz regularization constrains the gradient of the detector with respect to the input in order to increase robustness to input perturbations. The DIP defense removes perturbations using generative convolutional neural networks in an unsupervised manner. Regularization improved the detection of perturbed deepfakes on average, including a 10% accuracy boost in the blackbox case. The DIP defense achieved 95% accuracy on perturbed deepfakes that fooled the original detector while retaining 98% accuracy in other cases on a 100 image subsample.
Younus, M. A., Hasan, T. M..  2020.  Effective and Fast DeepFake Detection Method Based on Haar Wavelet Transform. 2020 International Conference on Computer Science and Software Engineering (CSASE). :186—190.
DeepFake using Generative Adversarial Networks (GANs) tampered videos reveals a new challenge in today's life. With the inception of GANs, generating high-quality fake videos becomes much easier and in a very realistic manner. Therefore, the development of efficient tools that can automatically detect these fake videos is of paramount importance. The proposed DeepFake detection method takes the advantage of the fact that current DeepFake generation algorithms cannot generate face images with varied resolutions, it is only able to generate new faces with a limited size and resolution, a further distortion and blur is needed to match and fit the fake face with the background and surrounding context in the source video. This transformation causes exclusive blur inconsistency between the generated face and its background in the outcome DeepFake videos, in turn, these artifacts can be effectively spotted by examining the edge pixels in the wavelet domain of the faces in each frame compared to the rest of the frame. A blur inconsistency detection scheme relied on the type of edge and the analysis of its sharpness using Haar wavelet transform as shown in this paper, by using this feature, it can determine if the face region in a video has been blurred or not and to what extent it has been blurred. Thus will lead to the detection of DeepFake videos. The effectiveness of the proposed scheme is demonstrated in the experimental results where the “UADFV” dataset has been used for the evaluation, a very successful detection rate with more than 90.5% was gained.
Zhu, K., Wu, B., Wang, B..  2020.  Deepfake Detection with Clustering-based Embedding Regularization. 2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC). :257—264.
In recent months, AI-synthesized face swapping videos referred to as deepfake have become an emerging problem. False video is becoming more and more difficult to distinguish, which brings a series of challenges to social security. Some scholars are devoted to studying how to improve the detection accuracy of deepfake video. At the same time, in order to conduct better research, some datasets for deepfake detection are made. Companies such as Google and Facebook have also spent huge sums of money to produce datasets for deepfake video detection, as well as holding deepfake detection competitions. The continuous advancement of video tampering technology and the improvement of video quality have also brought great challenges to deepfake detection. Some scholars have achieved certain results on existing datasets, while the results on some high-quality datasets are not as good as expected. In this paper, we propose new method with clustering-based embedding regularization for deepfake detection. We use open source algorithms to generate videos which can simulate distinctive artifacts in the deepfake videos. To improve the local smoothness of the representation space, we integrate a clustering-based embedding regularization term into the classification objective, so that the obtained model learns to resist adversarial examples. We evaluate our method on three latest deepfake datasets. Experimental results demonstrate the effectiveness of our method.
Khodabakhsh, A., Busch, C..  2020.  A Generalizable Deepfake Detector based on Neural Conditional Distribution Modelling. 2020 International Conference of the Biometrics Special Interest Group (BIOSIG). :1—5.
Photo- and video-realistic generation techniques have become a reality following the advent of deep neural networks. Consequently, there are immense concerns regarding the difficulty in differentiating what content is real from what is synthetic. An example of video-realistic generation techniques is the infamous Deepfakes, which exploit the main modality by which humans identify each other. Deepfakes are a category of synthetic face generation methods and are commonly based on generative adversarial networks. In this article, we propose a novel two-step synthetic face image detection method in which general-purpose features are extracted in a first step, trivializing the task of detecting synthetic images. The anomaly detector predicts the conditional probabilities for observing every individual pixel in the image and is trained on pristine data only. The extracted anomaly features demonstrate true generalization capacity across widely different unknown synthesis methods while showing a minimal loss in performance with regard to the detection of known synthetic samples.
Maksutov, A. A., Morozov, V. O., Lavrenov, A. A., Smirnov, A. S..  2020.  Methods of Deepfake Detection Based on Machine Learning. 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :408—411.
Nowadays, people faced an emerging problem of AI-synthesized face swapping videos, widely known as the DeepFakes. This kind of videos can be created to cause threats to privacy, fraudulence and so on. Sometimes good quality DeepFake videos recognition could be hard to distinguish with people eyes. That's why researchers need to develop algorithms to detect them. In this work, we present overview of indicators that can tell us about the fact that face swapping algorithms were used on photos. Main purpose of this paper is to find algorithm or technology that can decide whether photo was changed with DeepFake technology or not with good accuracy.
Rana, M. S., Sung, A. H..  2020.  DeepfakeStack: A Deep Ensemble-based Learning Technique for Deepfake Detection. 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). :70—75.
Recent advances in technology have made the deep learning (DL) models available for use in a wide variety of novel applications; for example, generative adversarial network (GAN) models are capable of producing hyper-realistic images, speech, and even videos, such as the so-called “Deepfake” produced by GANs with manipulated audio and/or video clips, which are so realistic as to be indistinguishable from the real ones in human perception. Aside from innovative and legitimate applications, there are numerous nefarious or unlawful ways to use such counterfeit contents in propaganda, political campaigns, cybercrimes, extortion, etc. To meet the challenges posed by Deepfake multimedia, we propose a deep ensemble learning technique called DeepfakeStack for detecting such manipulated videos. The proposed technique combines a series of DL based state-of-art classification models and creates an improved composite classifier. Based on our experiments, it is shown that DeepfakeStack outperforms other classifiers by achieving an accuracy of 99.65% and AUROC of 1.0 score in detecting Deepfake. Therefore, our method provides a solid basis for building a Realtime Deepfake detector.
Nguyen, H. M., Derakhshani, R..  2020.  Eyebrow Recognition for Identifying Deepfake Videos. 2020 International Conference of the Biometrics Special Interest Group (BIOSIG). :1—5.
Deepfake imagery that contains altered faces has become a threat to online content. Current anti-deepfake approaches usually do so by detecting image anomalies, such as visible artifacts or inconsistencies. However, with deepfake advances, these visual artifacts are becoming harder to detect. In this paper, we show that one can use biometric eyebrow matching as a tool to detect manipulated faces. Our method could provide an 0.88 AUC and 20.7% EER for deepfake detection when applied to the highest quality deepfake dataset, Celeb-DF.
2020-12-17
Staschulat, J., Lütkebohle, I., Lange, R..  2020.  The rclc Executor: Domain-specific deterministic scheduling mechanisms for ROS applications on microcontrollers: work-in-progress. 2020 International Conference on Embedded Software (EMSOFT). :18—19.

Robots are networks of a variety of computing devices, such as powerful computing platforms but also tiny microcontrollers. The Robot Operating System (ROS) is the dominant framework for powerful computing devices. While ROS version 2 adds important features like quality of service and security, it cannot be directly applied to microcontrollers because of its large memory footprint. The micro-ROS project has ported the ROS 2 API to microcontrollers. However, the standard ROS 2 concepts are not enough for real-time performance: In the ROS 2 release “Foxy”, the standard ROS 2 Executor, which is the central component responsible for handling timers and incoming message data, is neither real-time capable nor deterministic. Domain-specific requirements of mobile robots, like sense-plan-act control loops, cannot be addressed with the standard ROS 2 Executor. In this paper, we present an advanced Executor for the ROS 2 C API which provides deterministic scheduling and supports domain-specific requirements. A proof-of-concept is demonstrated on a 32-bit microcontroller.

Kumar, R., Sarupria, G., Panwala, V., Shah, S., Shah, N..  2020.  Power Efficient Smart Home with Voice Assistant. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1—5.

The popularity and demand of home automation has increased exponentially in recent years because of the ease it provides. Recently, development has been done in this domain and few systems have been proposed that either use voice assistants or application for controlling the electrical appliances. However; less emphasis is laid on power efficiency and this system cannot be integrated with the existing appliances and hence, the entire system needs to be upgraded adding to a lot of additional cost in purchasing new appliances. In this research, the objective is to design such a system that emphasises on power efficiency as well as can be integrated with the already existing appliances. NodeMCU, along with Raspberry Pi, Firebase realtime database, is used to create a system that accomplishes such endeavours and can control relays, which can control these appliances without the need of replacing them. The experiments in this paper demonstrate triggering of electrical appliances using voice assistant, fire alarm on the basis of flame sensor and temperature sensor. Moreover; use of android application was presented for operating electrical appliances from a remote location. Lastly, the system can be modified by adding security cameras, smart blinds, robot vacuums etc.

Sun, P., Garcia, L., Salles-Loustau, G., Zonouz, S..  2020.  Hybrid Firmware Analysis for Known Mobile and IoT Security Vulnerabilities. 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :373—384.

Mobile and IoT operating systems–and their ensuing software updates–are usually distributed as binary files. Given that these binary files are commonly closed source, users or businesses who want to assess the security of the software need to rely on reverse engineering. Further, verifying the correct application of the latest software patches in a given binary is an open problem. The regular application of software patches is a central pillar for improving mobile and IoT device security. This requires developers, integrators, and vendors to propagate patches to all affected devices in a timely and coordinated fashion. In practice, vendors follow different and sometimes improper security update agendas for both mobile and IoT products. Moreover, previous studies revealed the existence of a hidden patch gap: several vendors falsely reported that they patched vulnerabilities. Therefore, techniques to verify whether vulnerabilities have been patched or not in a given binary are essential. Deep learning approaches have shown to be promising for static binary analyses with respect to inferring binary similarity as well as vulnerability detection. However, these approaches fail to capture the dynamic behavior of these systems, and, as a result, they may inundate the analysis with false positives when performing vulnerability discovery in the wild. In particular, they cannot capture the fine-grained characteristics necessary to distinguish whether a vulnerability has been patched or not. In this paper, we present PATCHECKO, a vulnerability and patch presence detection framework for executable binaries. PATCHECKO relies on a hybrid, cross-platform binary code similarity analysis that combines deep learning-based static binary analysis with dynamic binary analysis. PATCHECKO does not require access to the source code of the target binary nor that of vulnerable functions. We evaluate PATCHECKO on the most recent Google Pixel 2 smartphone and the Android Things IoT firmware images, within which 25 known CVE vulnerabilities have been previously reported and patched. Our deep learning model shows a vulnerability detection accuracy of over 93%. We further prune the candidates found by the deep learning stage–which includes false positives–via dynamic binary analysis. Consequently, PATCHECKO successfully identifies the correct matches among the candidate functions in the top 3 ranked outcomes 100% of the time. Furthermore, PATCHECKO's differential engine distinguishes between functions that are still vulnerable and those that are patched with an accuracy of 96%.

Lu, W., Shu, S., Shi, H., Li, R., Dong, W..  2020.  Synthesizing Secure Reactive Controller for Unmanned Aerial System. 2019 6th International Conference on Dependable Systems and Their Applications (DSA). :419—424.

Complex CPS such as UAS got rapid development these years, but also became vulnerable to GPS spoofing, packets injection, buffer-overflow and other malicious attacks. Ensuring the behaviors of UAS always keeping secure no matter how the environment changes, would be a prospective direction for UAS security. This paper aims at presenting a reactive synthesis-based approach to implement the automatic generation of secure UAS controller. First, we study the operating mechanism of UAS and construct a high-Ievel model consisting of actuator and monitor. Besides, we analyze the security threats of UAS from the perspective of hardware, software and data transmission, and then extract the corresponding specifications of security properties with LTL formulas. Based on the UAS model and security specifications, the controller can be constructed by GR(1) synthesis algorithm, which is a two-player game process between UAV and Environment. Finally, we expand the function of LTLMoP platform to construct the automatons for controller in multi-robots system, which provides secure behavior strategies under several typical UAS attack scenarios.

Lee, J., Chen, H., Young, J., Kim, H..  2020.  RISC-V FPGA Platform Toward ROS-Based Robotics Application. 2020 30th International Conference on Field-Programmable Logic and Applications (FPL). :370—370.

RISC-V is free and open standard instruction set architecture following reduced instruction set computer principle. Because of its openness and scalability, RISC-V has been adapted not only for embedded CPUs such as mobile and IoT market, but also for heavy-workload CPUs such as the data center or super computing field. On top of it, Robotics is also a good application of RISC-V because security and reliability become crucial issues of robotics system. These problems could be solved by enthusiastic open source community members as they have shown on open source operating system. However, running RISC-V on local FPGA becomes harder than before because now RISC-V foundation are focusing on cloud-based FPGA environment. We have experienced that recently released OS and toolchains for RISC-V are not working well on the previous CPU image for local FPGA. In this paper we design the local FPGA platform for RISC-V processor and run the robotics application on mainstream Robot Operating System on top of the RISC-V processor. This platform allow us to explore the architecture space of RISC-V CPU for robotics application, and get the insight of the RISC-V CPU architecture for optimal performance and the secure system.

Iskhakov, A., Jharko, E..  2020.  Approach to Security Provision of Machine Vision for Unmanned Vehicles of “Smart City”. 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). :1—5.

By analogy to nature, sight is the main integral component of robotic complexes, including unmanned vehicles. In this connection, one of the urgent tasks in the modern development of unmanned vehicles is the solution to the problem of providing security for new advanced systems, algorithms, methods, and principles of space navigation of robots. In the paper, we present an approach to the protection of machine vision systems based on technologies of deep learning. At the heart of the approach lies the “Feature Squeezing” method that works on the phase of model operation. It allows us to detect “adversarial” examples. Considering the urgency and importance of the target process, the features of unmanned vehicle hardware platforms and also the necessity of execution of tasks on detecting of the objects in real-time mode, it was offered to carry out an additional simple computational procedure of localization and classification of required objects in case of crossing a defined in advance threshold of “adversarial” object testing.

Promyslov, V., Semenkov, K..  2020.  Security Threats for Autonomous and Remotely Controlled Vehicles in Smart City. 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). :1—5.

The paper presents a comprehensive model of cybersecurity threats for a system of autonomous and remotely controlled vehicles (AV) in the environment of a smart city. The main focus in the security context is given to the “integrity” property. That property is of higher importance for industrial control systems in comparison with other security properties (availability and confidentiality). The security graph, which is part of the model, is dynamic, and, in real cases, its analysis may require significant computing resources for AV systems with a large number of assets and connections. The simplified example of the security graph for the AV system is presented.

Gao, X., Fu, X..  2020.  Miniature Water Surface Garbage Cleaning Robot. 2020 International Conference on Computer Engineering and Application (ICCEA). :806—810.

In light of the problem for garbage cleaning in small water area, an intelligent miniature water surface garbage cleaning robot with unmanned driving and convenient operation is designed. Based on STC12C5A60S2 as the main controller in the design, power module, transmission module and cleaning module are controlled together to realize the function of cleaning and transporting garbage, intelligent remote control of miniature water surface garbage cleaning robot is realized by the WiFi module. Then the prototype is developed and tested, which will verify the rationality of the design. Compared with the traditional manual driving water surface cleaning devices, the designed robot realizes the intelligent control of unmanned driving, and achieves the purpose of saving human resources and reducing labor intensity, and the system operates security and stability, which has certain practical value.

Amrouche, F., Lagraa, S., Frank, R., State, R..  2020.  Intrusion detection on robot cameras using spatio-temporal autoencoders: A self-driving car application. 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). :1—5.

Robot Operating System (ROS) is becoming more and more important and is used widely by developers and researchers in various domains. One of the most important fields where it is being used is the self-driving cars industry. However, this framework is far from being totally secure, and the existing security breaches do not have robust solutions. In this paper we focus on the camera vulnerabilities, as it is often the most important source for the environment discovery and the decision-making process. We propose an unsupervised anomaly detection tool for detecting suspicious frames incoming from camera flows. Our solution is based on spatio-temporal autoencoders used to truthfully reconstruct the camera frames and detect abnormal ones by measuring the difference with the input. We test our approach on a real-word dataset, i.e. flows coming from embedded cameras of self-driving cars. Our solution outperforms the existing works on different scenarios.

charan, S. S., karuppaiah, D..  2020.  Operating System Process Using Message Passing Concept in Military. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). :1—4.

In Robotics Operating System Process correspondence is the instrument given by the working framework that enables procedures to speak with one another Message passing model enables different procedures to peruse and compose information to the message line without being associated with one another, messages going between Robots. ROS is intended to be an inexactly coupled framework where a procedure is known as a hub and each hub ought to be answerable for one assignment. In the military application robots will go to go about as an officer and going ensure nation. In the referenced idea robot solider will give the message passing idea then the officers will go caution and start assaulting on the foes.

Hu, Z., Niu, J., Ren, T., Li, H., Rui, Y., Qiu, Y., Bai, L..  2020.  A Resource Management Model for Real-time Edge System of Multiple Robots. 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). :222—227.

Industrial robots are playing an important role in now a day industrial productions. However, due to the increasing in robot hardware modules and the rapid expansion of software modules, the reliability of operating systems for industrial robots is facing severe challenges, especially for the light-weight edge computing platforms. Based on current technologies on resource security isolation protection and access control, a novel resource management model for real-time edge system of multiple robot arms is proposed on light-weight edge devices. This novel resource management model can achieve the following functions: mission-critical resource classification, resource security access control, and multi-level security data isolation transmission. We also propose a fault location and isolation model on each lightweight edge device, which ensures the reliability of the entire system. Experimental results show that the robot operating system can meet the requirements of hierarchical management and resource access control. Compared with the existing methods, the fault location and isolation model can effectively locate and deal with the faults generated by the system.

Zong, Y., Guo, Y., Chen, X..  2019.  Policy-Based Access Control for Robotic Applications. 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE). :368—3685.

With the wide application of modern robots, more concerns have been raised on security and privacy of robotic systems and applications. Although the Robot Operating System (ROS) is commonly used on different robots, there have been few work considering the security aspects of ROS. As ROS does not employ even the basic permission control mechanism, applications can access any resources without limitation, which could result in equipment damage, harm to human, as well as privacy leakage. In this paper we propose an access control mechanism for ROS based on an extended policy-based access control (PBAC) model. Specifically, we extend ROS to add an additional node dedicated for access control so that it can provide user identity and permission management services. The proposed mechanism also allows the administrator to revoke a permission dynamically. We implemented the proposed method in ROS and demonstrated its applicability and performance through several case studies.

Wehbe, R., Williams, R. K..  2019.  Approximate Probabilistic Security for Networked Multi-Robot Systems. 2019 International Conference on Robotics and Automation (ICRA). :1997—2003.

In this paper, we formulate a combinatorial optimization problem that aims to maximize the accuracy of a lower bound estimate of the probability of security of a multi-robot system (MRS), while minimizing the computational complexity involved in its calculation. Security of an MRS is defined using the well-known control theoretic notion of left invertiblility, and the probability of security of an MRS can be calculated using binary decision diagrams (BDDs). The complexity of a BDD depends on the number of disjoint path sets considered during its construction. Taking into account all possible disjoint paths results in an exact probability of security, however, selecting an optimal subset of disjoint paths leads to a good estimate of the probability while significantly reducing computation. To deal with the dynamic nature of MRSs, we introduce two methods: (1) multi-point optimization, a technique that requires some a priori knowledge of the topology of the MRS over time, and (2) online optimization, a technique that does not require a priori knowledge, but must construct BDDs while the MRS is operating. Finally, our approach is validated on an MRS performing a rendezvous objective while exchanging information according to a noisy state agreement process.