Alexander Aved

Deeraj Nagothu, Ronghua Xu, Yu Chen et al.

Advancements in generative models, like Deepfake allows users to imitate a targeted person and manipulate online interactions. It has been recognized that disinformation may cause disturbance in society and ruin the foundation of trust. This article presents DeFakePro, a decentralized consensus mechanism-based Deepfake detection technique in online video conferencing tools. Leveraging Electrical Network Frequency (ENF), an environmental fingerprint embedded in digital media recording, affords a consensus mechanism design called Proof-of-ENF (PoENF) algorithm. The similarity in ENF signal fluctuations is utilized in the PoENF algorithm to authenticate the media broadcasted in conferencing tools. By utilizing the video conferencing setup with malicious participants to broadcast deep fake video recordings to other participants, the DeFakePro system verifies the authenticity of the incoming media in both audio and video channels.

Nora Agah, Javad Mohammadi, Alex Aved et al.

As the complexities of Dynamic Data Driven Applications Systems increase, preserving their resilience becomes more challenging. For instance, maintaining power grid resilience is becoming increasingly complicated due to the growing number of stochastic variables (such as renewable outputs) and extreme weather events that add uncertainty to the grid. Current optimization methods have struggled to accommodate this rise in complexity. This has fueled the growing interest in data-driven methods used to operate the grid, leading to more vulnerability to cyberattacks. One such disruption that is commonly discussed is the adversarial disruption, where the intruder attempts to add a small perturbation to input data in order to "manipulate" the system operation. During the last few years, work on adversarial training and disruptions on the power system has gained popularity. In this paper, we will first review these applications, specifically on the most common types of adversarial disruptions: evasion and poisoning disruptions. Through this review, we highlight the gap between poisoning and evasion research when applied to the power grid. This is due to the underlying assumption that model training is secure, leading to evasion disruptions being the primary type of studied disruption. Finally, we will examine the impacts of data poisoning interventions and showcase how they can endanger power grid resilience.

Ronghua Xu, Yu Chen, Erik Blasch et al.

Advancement in artificial intelligence (AI) and machine learning (ML), dynamic data driven application systems (DDDAS), and hierarchical cloud-fog-edge computing paradigm provide opportunities for enhancing multi-domain systems performance. As one example that represents multi-domain scenario, a "fly-by-feel" system utilizes DDDAS framework to support autonomous operations and improve maneuverability, safety and fuel efficiency. The DDDAS "fly-by-feel" avionics system can enhance multi-domain coordination to support domain specific operations. However, conventional enabling technologies rely on a centralized manner for data aggregation, sharing and security policy enforcement, and it incurs critical issues related to bottleneck of performance, data provenance and consistency. Inspired by the containerized microservices and blockchain technology, this paper introduces BLEM, a hybrid BLockchain-Enabled secure Microservices fabric to support decentralized, secure and efficient data fusion and multi-domain operations for avionics systems. Leveraging the fine-granularity and loose-coupling features of the microservices architecture, multidomain operations and security functionalities are decoupled into multiple containerized microservices. A hybrid blockchain fabric based on two-level committee consensus protocols is proposed to enable decentralized security architecture and support immutability, auditability and traceability for data provenience in existing multi-domain avionics system. Our evaluation results show the feasibility of the proposed BLEM mechanism to support decentralized security service and guarantee immutability, auditability and traceability for data provenience across domain boundaries.

Seyed Yahya Nikouei, Yu Chen, Alexander Aved et al.

Situation AWareness (SAW) is essential for many mission critical applications. However, SAW is very challenging when trying to immediately identify objects of interest or zoom in on suspicious activities from thousands of video frames. This work aims at developing a queryable system to instantly select interesting content. While face recognition technology is mature, in many scenarios like public safety monitoring, the features of objects of interest may be much more complicated than face features. In addition, human operators may not be always able to provide a descriptive, simple, and accurate query. Actually, it is more often that there are only rough, general descriptions of certain suspicious objects or accidents. This paper proposes an Interactive Video Surveillance as an Edge service (I-ViSE) based on unsupervised feature queries. Adopting unsupervised methods that do not reveal any private information, the I-ViSE scheme utilizes general features of a human body and color of clothes. An I-ViSE prototype is built following the edge-fog computing paradigm and the experimental results verified the I-ViSE scheme meets the design goal of scene recognition in less than two seconds.

Seyed Yahya Nikouei, Yu Chen, Alexander Aved et al.

Urban imagery usually serves as forensic analysis and by design is available for incident mitigation. As more imagery collected, it is harder to narrow down to certain frames among thousands of video clips to a specific incident. A real-time, proactive surveillance system is desirable, which could instantly detect dubious personnel, identify suspicious activities, or raise momentous alerts. The recent proliferation of the edge computing paradigm allows more data-intensive tasks to be accomplished by smart edge devices with lightweight but powerful algorithms. This paper presents a forensic surveillance strategy by introducing an Instant Suspicious Activity identiFication at the Edge (I-SAFE) using fuzzy decision making. A fuzzy control system is proposed to mimic the decision-making process of a security officer. Decisions are made based on video features extracted by a lightweight Deep Machine Learning (DML) model. Based on the requirements from the first-line law enforcement officers, several features are selected and fuzzified to cope with the state of uncertainty that exists in the officers' decision-making process. Using features in the edge hierarchy minimizes the communication delay such that instant alerting is achieved. Additionally, leveraging the Microservices architecture, the I-SAFE scheme possesses good scalability given the increasing complexities at the network edge. Implemented as an edge-based application and tested using exemplary and various labeled dataset surveillance videos, the I-SAFE scheme raises alerts by identifying the suspicious activity in an average of 0.002 seconds. Compared to four other state-of-the-art methods over two other data sets, the experimental study verified the superiority of the I-SAFE decentralized method.

Seyed Yahya Nikouei, Ronghua Xu, Yu Chen et al.

Connected societies require reliable measures to assure the safety, privacy, and security of members. Public safety technology has made fundamental improvements since the first generation of surveillance cameras were introduced, which aims to reduce the role of observer agents so that no abnormality goes unnoticed. While the edge computing paradigm promises solutions to address the shortcomings of cloud computing, e.g., the extra communication delay and network security issues, it also introduces new challenges. One of the main concerns is the limited computing power at the edge to meet the on-site dynamic data processing. In this paper, a Lightweight IoT (Internet of Things) based Smart Public Safety (LISPS) framework is proposed on top of microservices architecture. As a computing hierarchy at the edge, the LISPS system possesses high flexibility in the design process, loose coupling to add new services or update existing functions without interrupting the normal operations, and efficient power balancing. A real-world public safety monitoring scenario is selected to verify the effectiveness of LISPS, which detects, tracks human objects and identify suspicious activities. The experimental results demonstrate the feasibility of the approach.

Seyed Yahya Nikouei, Ronghua Xu, Deeraj Nagothu et al.

Information from surveillance video is essential for situational awareness (SAW). Nowadays, a prohibitively large amount of surveillance data is being generated continuously by ubiquitously distributed video sensors. It is very challenging to immediately identify the objects of interest or zoom in suspicious actions from thousands of video frames. Making the big data indexable is critical to tackle this problem. It is ideal to generate pattern indexes in a real-time, on-site manner on the video streaming instead of depending on the batch processing at the cloud centers. The modern edge-fog-cloud computing paradigm allows implementation of time sensitive tasks at the edge of the network. The on-site edge devices collect the information sensed in format of frames and extracts useful features. The near-site fog nodes conduct the contextualization and classification of the features. The remote cloud center is in charge of more data intensive and computing intensive tasks. However, exchanging the index information among devices in different layers raises security concerns where an adversary can capture or tamper with features to mislead the surveillance system. In this paper, a blockchain enabled scheme is proposed to protect the index data through an encrypted secure channel between the edge and fog nodes. It reduces the chance of attacks on the small edge and fog devices. The feasibility of the proposal is validated through intensive experimental analysis.

Li Sulimowicz, Ishfaq Ahmad, Alexander Aved

Superpixel-based Higher-order Conditional Random Fields (CRFs) are effective in enforcing long-range consistency in pixel-wise labeling problems, such as semantic segmentation. However, their major short coming is considerably longer time to learn higher-order potentials and extra hyperparameters and/or weights compared with pairwise models. This paper proposes a superpixel-enhanced pairwise CRF framework that consists of the conventional pairwise as well as our proposed superpixel-enhanced pairwise (SP-Pairwise) potentials. SP-Pairwise potentials incorporate the superpixel-based higher-order cues by conditioning on a segment filtered image and share the same set of parameters as the conventional pairwise potentials. Therefore, the proposed superpixel-enhanced pairwise CRF has a lower time complexity in parameter learning and at the same time it outperforms higher-order CRF in terms of inference accuracy. Moreover, the new scheme takes advantage of the pre-trained pairwise models by reusing their parameters and/or weights, which provides a significant accuracy boost on the basis of CRF-RNN even without training. Experiments on MSRC-21 and PASCAL VOC 2012 dataset confirm the effectiveness of our method.

Li Sulimowicz, Ishfaq Ahmad, Alexander Aved

Superpixel-based Higher-order Conditional random fields (SP-HO-CRFs) are known for their effectiveness in enforcing both short and long spatial contiguity for pixelwise labelling in computer vision. However, their higher-order potentials are usually too complex to learn and often incur a high computational cost in performing inference. We propose an new approximation approach to SP-HO-CRFs that resolves these problems. Our approach is a multi-layer CRF framework that inherits the simplicity from pairwise CRFs by formulating both the higher-order and pairwise cues into the same pairwise potentials in the first layer. Essentially, this approach provides accuracy enhancement on the basis of pairwise CRFs without training by reusing their pre-trained parameters and/or weights. The proposed multi-layer approach performs especially well in delineating the boundary details (boarders) of object categories such as "trees" and "bushes". Multiple sets of experiments conducted on dataset MSRC-21 and PASCAL VOC 2012 validate the effectiveness and efficiency of the proposed methods.