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Register a new userTracking Counterfeit Cryptocurrency End-to-end
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Added by
Bingyu Gao
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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The production of counterfeit money has a long history. It refers to the creation of imitation currency that is produced without the legal sanction of government. With the growth of the cryptocurrency ecosystem, there is expanding evidence that counterfeit cryptocurrency has also appeared. In this paper, we empirically explore the presence of counterfeit cryptocurrencies on Ethereum and measure their impact. By analyzing over 190K ERC-20 tokens (or cryptocurrencies) on Ethereum, we have identified 2, 117 counterfeit tokens that target 94 of the 100 most popular cryptocurrencies. We perform an end-to-end characterization of the counterfeit token ecosystem, including their popularity, creators and holders, fraudulent behaviors and advertising channels. Through this, we have identified two types of scams related to counterfeit tokens and devised techniques to identify such scams. We observe that over 7,104 victims were deceived in these scams, and the overall financial loss sums to a minimum of $ 17 million (74,271.7 ETH). Our findings demonstrate the urgency to identify counterfeit cryptocurrencies and mitigate this threat.
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Estimating eye-gaze from images alone is a challenging task, in large parts due to un-observable person-specific factors. Achieving high accuracy typically requires labeled data from test users which may not be attainable in real applications. We observe that there exists a strong relationship between what users are looking at and the appearance of the users eyes. In response to this understanding, we propose a novel dataset and accompanying method which aims to explicitly learn these semantic and temporal relationships. Our video dataset consists of time-synchronized screen recordings, user-facing camera views, and eye gaze data, which allows for new benchmarks in temporal gaze tracking as well as label-free refinement of gaze. Importantly, we demonstrate that the fusion of information from visual stimuli as well as eye images can lead towards achieving performance similar to literature-reported figures acquired through supervised personalization. Our final method yields significant performance improvements on our proposed EVE dataset, with up to a 28 percent improvement in Point-of-Gaze estimates (resulting in 2.49 degrees in angular error), paving the path towards high-accuracy screen-based eye tracking purely from webcam sensors. The dataset and reference source code are available at https://ait.ethz.ch/projects/2020/EVE
The key challenge in multiple-object tracking task is temporal modeling of the object under track. Existing tracking-by-detection methods adopt simple heuristics, such as spatial or appearance similarity. Such methods, in spite of their commonality, are overly simple and lack the ability to learn temporal variations from data in an end-to-end manner. In this paper, we present MOTR, a fully end-to-end multiple-object tracking framework. It learns to model the long-range temporal variation of the objects. It performs temporal association implicitly and avoids previous explicit heuristics. Built upon DETR, MOTR introduces the concept of track query. Each track query models the entire track of an object. It is transferred and updated frame-by-frame to perform iterative predictions in a seamless manner. Tracklet-aware label assignment is proposed for one-to-one assignment between track queries and object tracks. Temporal aggregation network together with collective average loss is further proposed to enhance the long-range temporal relation. Experimental results show that MOTR achieves competitive performance and can serve as a strong Transformer-based baseline for future research. Code is available at url{https://github.com/megvii-model/MOTR}.
Over the last years, security kernels have played a promising role in reshaping the landscape of platform security on todays ubiquitous embedded devices. Security kernels, such as separation kernels, enable constructing high-assurance mixed-criticality execution platforms. They reduce the software portion of the systems trusted computing base to a thin layer, which enforces isolation between low- and high-criticality components. The reduced trusted computing base minimizes the system attack surface and facilitates the use of formal methods to ensure functional correctness and security of the kernel. In this thesis, we explore various aspects of building a provably secure separation kernel using virtualization technology. In particular, we examine techniques related to the appropriate management of the memory subsystem. Once these techniques were implemented and functionally verified, they provide reliable a foundation for application scenarios that require strong guarantees of isolation and facilitate formal reasoning about the systems overall security.
In this paper, we present an end-to-end view of IoT security and privacy and a case study. Our contribution is three-fold. First, we present our end-to-end view of an IoT system and this view can guide risk assessment and design of an IoT system. We identify 10 basic IoT functionalities that are related to security and privacy. Based on this view, we systematically present security and privacy requirements in terms of IoT system, software, networking and big data analytics in the cloud. Second, using the end-to-end view of IoT security and privacy, we present a vulnerability analysis of the Edimax IP camera system. We are the first to exploit this system and have identified various attacks that can fully control all the cameras from the manufacturer. Our real-world experiments demonstrate the effectiveness of the discovered attacks and raise the alarms again for the IoT manufacturers. Third, such vulnerabilities found in the exploit of Edimax cameras and our previous exploit of Edimax smartplugs can lead to another wave of Mirai attacks, which can be either botnets or worm attacks. To systematically understand the damage of the Mirai malware, we model propagation of the Mirai and use the simulations to validate the modeling. The work in this paper raises the alarm again for the IoT device manufacturers to better secure their products in order to prevent malware attacks like Mirai.
The Correlation Filter is an algorithm that trains a linear template to discriminate between images and their translations. It is well suited to object tracking because its formulation in the Fourier domain provides a fast solution, enabling the detector to be re-trained once per frame. Previous works that use the Correlation Filter, however, have adopted features that were either manually designed or trained for a different task. This work is the first to overcome this limitation by interpreting the Correlation Filter learner, which has a closed-form solution, as a differentiable layer in a deep neural network. This enables learning deep features that are tightly coupled to the Correlation Filter. Experiments illustrate that our method has the important practical benefit of allowing lightweight architectures to achieve state-of-the-art performance at high framerates.
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