Applications such as autonomous vehicles and medical screening use deep learning models to localize and identify hundreds of objects in a single *** the past,it has been shown how an attacker can fool these models by ...
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Applications such as autonomous vehicles and medical screening use deep learning models to localize and identify hundreds of objects in a single *** the past,it has been shown how an attacker can fool these models by placing an adversarial patch within a ***,these patches must be placed in the target location and do not explicitly alter the semantics elsewhere in the *** this paper,we introduce a new type of adversarial patch which alters a model’s perception of an image’s *** patches can be placed anywhere within an image to change the classification or semantics of locations far from the *** call this new class of adversarial examples‘remote adversarial patches’(RAP).We implement our own RAP called IPatch and perform an in-depth analysis on without pixel clipping on image segmentation RAP attacks using five state-of-the-art architectures with eight different encoders on the CamVid street view ***,we demonstrate that the attack can be extended to object recognition models with preliminary results on the popular YOLOv3 *** found that the patch can change the classification of a remote target region with a success rate of up to 93%on average.
As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive...
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As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.
Attack vectors for adversaries have increased in organizations because of the growing use of less secure IoT devices. The risk of attacks on an organization’s network has also increased due to the bring your own devi...
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Recent decades have witnessed a trend that the echo state network(ESN)is widely utilized in field of time series prediction due to its powerful computational ***,most of the existing research on ESN is conducted under...
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Recent decades have witnessed a trend that the echo state network(ESN)is widely utilized in field of time series prediction due to its powerful computational ***,most of the existing research on ESN is conducted under the assumption that data is free of noise or polluted by the Gaussian noise,which lacks robustness or even fails to solve real-world *** work handles this issue by proposing a probabilistic regularized ESN(PRESN)with robustness ***,we design a novel objective function for minimizing both the mean and variance of modeling error,and then a scheme is derived for getting output weights of the ***,generalization performance,robustness,and unbiased estimation abilities of the PRESN are revealed by theoretical ***,experiments on a benchmark dataset and two real-world datasets are conducted to verify the performance of the proposed *** source code is publicly available at https://***/LongJinlab/probabilistic-regularized-echo-state-network.
Vision transformers have contributed greatly to advancements in the computer vision domain, demonstrating state-of-the-art performance in diverse tasks (e.g., image classification, object detection). However, their hi...
In multi-agent path finding (MAPF), agents must move from their current positions to their target positions without colliding. Prior work on MAPF commonly assumed perfect knowledge of the environment. We consider a MA...
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Today, the tourism industry is significantly impacted by mobile applications leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) to enhance tourists' experiences before, during, and after...
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Conversational agents (CA) are software programs that can converse with users using natural language. They are now widely used in various domains, such as tourism, healthcare, and others, to perform tasks and provide ...
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The development of chatbots utilizing artificial intelligence (AI) techniques represents a significant advancement in Natural Language Processing (NLP). Numerous studies employ deep learning and NLP methodologies to c...
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Unified Modeling Language (UML) is a versatile tool for specifying, visualizing, and documenting softwaresystems through diagrams. In the early stages of development, addressing design issues is critical to improving...
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