Defense Notices

Activism is a universal concept that has often played a major role in putting an end to injustices and human rights abuses globally. Political activism in specific is a modern day term coined to refer to a form of activism in which a group of people come into collision with a more omnipotent adversary - national or international governments - who often has a purview and control over the very telecommunications infrastructure that is necessary for activists in order to organize and operate. As technology and social media use have become vital to the success of activism movements in the twenty first century, our study focuses on surfacing the technical challenges and the defensive strategies that activists employ during a political revolution. We find that security and privacy behavior and app adoption is influenced by the specific societal and political context in which activists operate. In addition, the impact of a social media blockade or an internet blackout can trigger a series of anti-censorship approaches at scale and cripple activists’ technology use. To a large extent the combination of low tech defensive strategies employed by activists were sufficient against the threats of surveillance, arrests and device confiscation. Throughout our results we surface a number of design principles but also some design tensions that could occur between the security and usability needs of different populations. And thus, we present a set of observations that can help guide technology designers and policy makers. 

Recent advances in machine learning (ML), known as deep neural networks (DNN) or deep learning, have greatly improved the state-of-the-art for many ML tasks, such as image classification (He, Zhang, Ren, & Sun, 2016; Krizhevsky, Sutskever, & Hinton, 2012; LeCun, Bottou, Bengio, & Haffner, 1998; Szegedy et al., 2015; Zeiler & Fergus, 2014), speech recognition (Graves, Mohamed, & Hinton, 2013; Hannun et al., 2014; Hinton et al., 2012), complex games and learning from simple reward signals (Goodfellow et al., 2014; Mnih et al., 2015; Silver et al., 2016), and many other areas as well. NN and ML methods have been applied to the task of autonomously controlling a vehicle with only a camera image input to successfully navigate on road (Bojarski et al., 2016). However, advances in deep learning are not yet applied systematically to this task. In this work I used a simulated environment to implement and compare several methods for controlling autonomous navigation behavior using a standard camera input device to sense environmental state. The simulator contained a simulated car with a camera mounted on the top to gather visual data while being operated by a human controller on a virtual driving environment. The gathered data was used to perform supervised training for building an autonomous controller to drive the same vehicle remotely over a local connection. Reproduced past results that have used simple neural networks and other ML techniques to guide similar test vehicles using a camera. Compared these results with more complex deep neural network controllers, to see if they can improve navigation performance based on past methods on measures of speed, distance, and other performance metrics on unseen simulated road driving tasks.

Remote attestation is the act of making trust decisions about a communicating party. During this process, an appraiser asks a target to execute an attestation protocol that generates and returns evidence. The appraiser can then make claims about the target by evaluating the evidence. Copland is a formally specified, executable language for representing attestation protocols. We introduce Copland centered negotiation as prerequisite to attestation to find a protocol that meets the target’s needs for constrained disclosure and the appraiser’s desire for comprehensive information. Negotiation begins when the appraiser sends a request, a Copland phrase, to the target. The target gathers all protocols that satisfy the request and then, using their privacy policy, can filter out the phrases that expose sensitive information. The target sends these phrases to the appraiser as a proposal. The appraiser then chooses the best phrase for attestation, based on situational requirements embodied in a selection function. Our focus is statically ensuring the target does not share sensitive information though terms in the proposal, meeting their need for constrained disclosure. To accomplish this, we realize two independent implementation of the privacy and selection policies using indexed types and subset types. In using indexed types, the policy check is accomplishes by indexing the term grammar with the type of evidence the term produces. The statically ensures that terms written in the language will satisfy the privacy policy criteria. In using the subset type, we statically limit the collection of terms to those that satisfy the privacy policy. This type abides by the rules of set comprehension to build a set such that all elements of the set satisfy the privacy policy. Combining our ideas for a dependently typed privacy policy and negotiation, we give the target the chance to suggest a term or terms for attestation that fits the appraiser’s needs while not disclosing sensitive information.

COVID-19 is a disease that spreads from human to human which can be controlled by ensuring proper use of a facial mask. The spread of COVID-19 can be limited if people strictly maintain social distancing and use a facial mask. Very sadly, people are not obeying these rules properly which is speeding the spread of this virus. Detecting the people not obeying the rules and informing the corresponding authorities can be a solution in reducing the spread of Corona virus. The proposed method detects the face from the image correctly and then identifies if it has a mask on it or not. As a surveillance task performer, it can also detect a face along with a mask in motion. It has numerous applications, such as autonomous driving, education, surveillance, and so on.

Fatigue and microsleep at the wheel are often the cause of serious accidents and death. Fatigue, in general, is difficult to measure or observe unlike alcohol and drugs, which have clear key indicators and tests that are available easily. Hence, detection of driver’s fatigue and its indication is an active research area. Also, I believe that drowsiness can negatively impact people in working and classroom environments as well. Drowsiness in the workplace especially while working with heavy machinery may result in serious injuries similar to those that occur while driving drowsily. The proposed system for detecting driver drowsiness has a webcam that records the video of the driver and driver’s face is detected in each frame employing image processing techniques. Facial landmarks on the detected face are pointed and subsequently the eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their values, drowsiness is detected. If drowsiness is detected, a warning or alarm is sent to the driver from the warning system.

Fingerspelling in sign language has been the means of communicating technical terms and proper nouns when they do not have dedicated sign language gestures. The automatic recognition of fingerspelling can help resolve communication barriers when interacting with deaf people. The main challenges prevalent in automatic recognition tasks are the ambiguity in the gestures and strong articulation of the hands. The automatic recognition model should address high inter-class visual similarity and high intra-class variation in the gestures. Most of the existing research in fingerspelling recognition has focused on the dataset collected in a controlled environment. The recent collection of a large-scale annotated fingerspelling dataset in the wild, from social media and online platforms, captures the challenges in a real-world scenario. This study focuses on implementing a fine-grained visual attention approach using Transformer models to address the challenges existing in two fingerspelling recognition tasks: multiclass classification of static gestures and sequence-to-sequence prediction of continuous gestures. For a dataset with a single gesture in a controlled environment (multiclass classification), the Transformer decoder employs the textual description of gestures along with image features to achieve fine-grained attention. For the sequence-to-sequence prediction task in the wild dataset, fine-grained attention is attained by utilizing the change in motion of the video frames (optical flow) in sequential context-based attention along with a Transformer encoder model. The unsegmented continuous video dataset is jointly trained by balancing the Connectionist Temporal Classification (CTC) loss and maximum-entropy loss. The proposed methodologies outperform state-of-the-art performance in both datasets. In comparison to the previous work for static gestures in fingerspelling recognition, the proposed approach employs multimodal fine-grained visual categorization. The state-of-the-art model in sequence-to-sequence prediction employs an iterative zooming mechanism for fine-grained attention whereas the proposed method is able to capture better fine-grained attention in a single iteration.

With the rapid development of machine learning (ML) and deep learning (DL) methodologies, DL methods can be leveraged for wireless network reconfigurability and channel modeling. While deep learning-based methods have been applied in a few wireless network use cases, there is still much to be explored. In this project, we focus on the application of deep learning methods for two scenarios. In the first scenario, a user transmitter was moving randomly within a campus area, and at certain spots sending wireless signals that were received by multiple antennas. We construct an active deep learning architecture to predict user locations from received signals after dimensionality reduction, and analyze 4 traditional query strategies for active learning to improve the efficiency of utilizing labeled data. We propose a new location-based query strategy that considers both spatial density and model uncertainty when selecting samples to label. We show that the proposed query strategy outperforms all the existing strategies. In the second scenario, a reconfigurable intelligent surface (RIS) containing 4096 tunable cells reflects signals from a transmitter to users in an office for better performance. We use the training data of one user's received signals under different RIS configurations to learn the impact behavior of the RIS on the wireless channel. Based on the context and experience from the first scenario, we build a DL neural network that maps RIS configurations to received signal estimations. In the second phase, the loss function was customized towards our final evaluation formula to obtain the optimum configuration array for a user. We propose and build a customized DL pipeline that automatically learns the behavior of RIS on received signals, and generates the optimal RIS configuration array for each of the 50 test users.

Android's fragmented ecosystem makes the delivery of security updates and OS upgrades cumbersome and complex. While Google initiated various projects such as Android One, Project Treble, and Project Mainline to address this problem, and other involved entities (e.g., chipset vendors, manufacturers, carriers) continuously strive to improve their processes, it is still unclear how effective these efforts are on the delivery of updates to supported end-user devices. In this paper, we perform an extensive quantitative study (August 2015 to December 2019) to measure the Android security updates and OS upgrades rollout process. Our study leverages multiple data sources: the Android Open Source Project (AOSP), device manufacturers, and the top four U.S. carriers (AT\&T, Verizon, T-Mobile, and Sprint). Furthermore, we analyze an end-user dataset captured in 2019 (152M anonymized HTTP requests associated with 9.1M unique user identifiers) from a U.S.-based social network. Our findings include unique measurements that, due to the fragmented and inconsistent ecosystem, were previously challenging to perform. For example, manufacturers and carriers introduce a median latency of 24 days before rolling out security updates, with an additional median delay of 11 days before end devices update. We show that these values alter per carrier-manufacturer relationship, yet do not alter greatly based on a model's age. Our results also delve into the effectiveness of current Android projects. For instance, security updates for Treble devices are available on average 7 days faster than for non-Treble devices. While this constitutes an improvement, the security update delay for Treble devices still averages 19 days.

Solid-state transformers (SSTs) are comprised of several cascaded power stages with different voltage levels. This leads to more challenges for operation and maintenance of the SSTs not only under critical conditions, but also during normal operation. However, one of the most important reliability concerns for the SSTs is related to high voltage side switch and grid faults. High voltage stress on the switches, together with the fact that most modern SST topologies incorporate large number of power switches in the high voltage side, contribute to a higher probability of a switch fault occurrence. The power electronic switches in the high voltage stage are under very high voltage stress, significantly higher than other SST stages. Therefore, the probability of the switch failures becomes more substantial in this stage. In this research, a new technique is proposed to improve the overall reliability of the SSTs by enhancing the reliability of the high voltage stage.

The proposed method restores the normal operation of the SST from the point of view of the load even though the input stage voltages are unbalanced due to the switch faults. On the other hand, high voltage grid faults that result in unbalanced operating conditions in the SST can also lead to dire consequences in regards to safety and reliability. The proposed method can also revamp the faulty operation to the pre-fault conditions in the case of grid faults. The proposed method integrates the quasi-z-source inverter topology into the SST topology for rebalancing the transformer voltages. Therefore, this work develops a new SST topology in conjunction with a fault-tolerant operation strategy that can fully restore operation of the proposed SST in the case of the two fault scenarios. The proposed fault-tolerant operation strategy rebalances the line-to-line voltages after a fault occurrence by modifying the phase angles between the phase voltages generated by the high voltage stage of the proposed SST. The boosting property of the quasi-z-source inverter topology circuitry is then used to increase the amplitude of the rebalanced line-to-line voltages to their pre-fault values. A modified modulation technique is proposed for modifying the phase angles and controlling the quasi-z-source inverter topology shoot-through duty ratio.

Crowd estimation is an integral part of crowd analysis. It plays an important role in event management of huge gatherings like Hajj, sporting, and musical events or political rallies. Automated crowd count can lead to better and effective management of such events and prevent any unwanted incident. Crowd estimation is an active research problem due to different challenges pertaining to large perspective, huge variance in scale and image resolution, severe occlusions and dense crowd-like cluttered background regions. Current approaches cannot handle huge crowd diversity well and thus perform poorly in cases ranging from extreme low to high crowd-density, thus, leading to crowd underestimation or overestimation. Also, manual crowd counting subjects to very slow and inaccurate results due to the complex issues as mentioned above. To address the major issues and challenges in the crowd counting domain, we separately investigate two different types of input data: uni-modal (Image) and multi-modal (Image and Audio).

In the uni-modal setting, we propose and analyze four novel end-to-end crowd counting networks, ranging from multi-scale fusion-based models to uniscale one-pass and two-pass multi-task models. The multi-scale networks also employ the attention mechanism to enhance the model efficacy. On the other hand, the uni-scale models are equipped with novel and simple-yet-effective patch re-scaling module (PRM) that functions identical but lightweight in comparison to the multi-scale approaches. Experimental evaluation demonstrates that the proposed networks outperform the state-of-the-art methods in majority cases on four different benchmark datasets with up to 12.6% improvement in terms of the RMSE evaluation metric. Better cross-dataset performance also validates the better generalization ability of our schemes. For the multimodal input, the effective feature-extraction (FE) and strong information fusion between two modalities remain a big challenge. Thus, the aim in the multimodal environment is to investigate different fusion techniques with improved FE mechanism for better crowd estimation. The multi-scale uni-modal attention networks are also proven to be more effective in other deep leaning domains, as applied successfully on seven different scene-text recognition datasets with better performance.