Real time security framework for detecting abnormal events at ATM installations

Tripathi, Vikas; Mittal, Ankush; Gangodkar, Durgaprasad; Kanth, Vishnu

doi:10.1007/s11554-016-0573-3

Cited by 30 publications

(11 citation statements)

References 30 publications

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“…If the stride is equal to the clip size, there is no overlap between the clips that are considered as inputs for classification. On the other hand, if the stride is smaller than the size of the clip, which is often the case [6,8,42,43], the clips used in classification will overlap with each other as shown in Figure 4b. A stride that is smaller than the clip size will cause the same problem discussed above with respect to the temporal offset between the attackers' anticipation of the start point and the actual start point of the classifier.…”

Section: The Boundary Effectmentioning

confidence: 99%

“…In self-driving cars, video classification has been used to understand pedestrian actions and make navigation decisions [20]. Real-time video classification systems have also been deployed for automatic "fall detection" in elderly care facilities [48], and abnormal detection around automated teller machines [43]. All of these applications directly relate to the physical security or safety of people and property.…”

Section: Introductionmentioning

confidence: 99%

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Stealthy Adversarial Perturbations Against Real-Time Video Classification Systems

Li¹,

Neupane²,

Shrivastava³

et al. 2019

Proceedings 2019 Network and Distributed System Security Symposium

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Recent research has demonstrated the brittleness of machine learning systems to adversarial perturbations. However, the studies have been mostly limited to perturbations on images and more generally, classification that does not deal with temporally varying inputs. In this paper we ask "Are adversarial perturbations possible in real-time video classification systems and if so, what properties must they satisfy?" Such systems find application in surveillance applications, smart vehicles, and smart elderly care and thus, misclassification could be particularly harmful (e.g., a mishap at an elderly care facility may be missed). We show that accounting for temporal structure is key to generating adversarial examples in such systems. We exploit recent advances in generative adversarial network (GAN) architectures to account for temporal correlations and generate adversarial samples that can cause misclassification rates of over 80 % for targeted activities. More importantly, the samples also leave other activities largely unaffected making them extremely stealthy. Finally, we also surprisingly find that in many scenarios, the same perturbation can be applied to every frame in a video clip that makes the adversary's ability to achieve misclassification relatively easy.

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Section: The Boundary Effectmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stealthy Adversarial Perturbations Against Real-Time Video Classification Systems

Li¹,

Neupane²,

Shrivastava³

et al. 2019

Proceedings 2019 Network and Distributed System Security Symposium

View full text Add to dashboard Cite

show abstract

“…The moving target detection algorithm is to separate the moving target from the complex background. After research by many scholars in recent years, the moving target detection algorithm has made some progress, but there are still many interference factors in the complex actual scene that will affect the detection result of the moving target detection algorithm [29]. After continuous research by many scholars, we found that the detection accuracy and algorithm complexity of the moving target detection algorithm are improved.…”

Section: Introductionmentioning

confidence: 96%

Real-Time Detection and Spatial Segmentation of Difference Image Motion Changes

et al. 2020

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In this paper, real-time detection of moving targets is performed, and an improved moving target detection algorithm combining background modeling and three-frame difference method is studied. An improved anisotropic differential filtering algorithm is proposed. The algorithm mainly compares the gradient difference between the target and the background in eight nearby directions. Choose the average of the three directions with the smallest spread function value to filter the space, which will effectively highlight the difference between the targets. so that the target signal is well preserved in the differential space domain; then based on obtaining the difference map. The average gray level of the target and the local signal-to-noise ratio of the spatial domain obtained by the energy enhancement algorithm combining the spatial and temporal motion characteristics have reached 278 and 14.84 dB, which further improves the target signal. Strict registration of heterogeneous spatial domains is completed by affine transformation and bilinear interpolation, and in many cases, the invariance and robustness of the method are verified. Focus on the proposed color space fusion method based on spatial frequency and fuzzy transformation. The superiority of the methods is evaluated and compared from both subjective and objective aspects. Finally, it lays a good foundation for moving target detection and better dealing with complex scenes. The improved inter-frame difference method combined with background subtraction validates and compares the method in different scenarios. It shows the advanced nature of the method in both qualitative and quantitative aspects. And effectively overcome the shortcomings of the particle filter tracking algorithm based on a single feature.

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“…As the carrier of information transmission, voice, image, and video occupy a dominant position. Images are ubiquitous in people's lives and work [1]. Compared with the use of hearing to gather information, the use of vision to obtain information can be more comprehensive.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Simulation of Garden Image Visual Effect Based on Particle Swarm and Wavelet Threshold

Hua

Min

2020

IEEE Access

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This paper optimizes and simulates the visual effects of garden graphics based on particle swarm and wavelet threshold algorithm, and uses deep belief networks as the main body of the classifier. To make up for the shortcomings of the activation function of the deep belief network, this paper adopts the wavelet basis function as the activation function of the deep belief network to improve the recognition accuracy of the deep belief network, especially the recognition accuracy of small changes in the garden image information. It analyzes the learning process of deep belief network in detail, finds out the shortcomings of traditional algorithms, and optimizes them, combined with particle swarm optimization algorithm to construct a wavelet deep belief network model to further improve the recognition accuracy and recognition speed of garden image recognition. A hybrid optimization algorithm of genetic and particle swarm algorithms is proposed. The two algorithms complement each other and the idea of crossover and variation is introduced into the standard Particle Swarm Optimization (PSO) algorithm to avoid premature convergence of solutions obtained by the PSO algorithm and lead to local optimal solutions. Seven optimal solutions obtained by the algorithm are used as the weight of the sample features, which are multiplied by the sample features to obtain the input data of the weighted K-nearest neighbor algorithm. After that, a three-fold cross-validation method is utilized to train the data to ensure the classification effect of the data set. The weighted K-nearest neighbor algorithm has the best classification effect based on the hybrid optimization of genetic algorithms and particle swarm algorithm.

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Real time security framework for detecting abnormal events at ATM installations

Cited by 30 publications

References 30 publications

Stealthy Adversarial Perturbations Against Real-Time Video Classification Systems

Stealthy Adversarial Perturbations Against Real-Time Video Classification Systems

Real-Time Detection and Spatial Segmentation of Difference Image Motion Changes

Optimization and Simulation of Garden Image Visual Effect Based on Particle Swarm and Wavelet Threshold

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