Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

This work is the first comprehensive review about visual privacy protection.Several categories are established for all of the available privacy protection methods.A review of intelligent monitoring systems that takes privacy into account is provided.An overall vision of the state of the art is given to introduce novel researchers.A critical discussion about relevant key topics and open issues is provided. Recent advances in computer vision technologies have made possible the development of intelligent monitoring systems for video surveillance and ambient-assisted living. By using this technology, these systems are able to automatically interpret visual data from the environment and perform tasks that would have been unthinkable years ago. These achievements represent a radical improvement but they also suppose a new threat to individual's privacy. The new capabilities of such systems give them the ability to collect and index a huge amount of private information about each individual. Next-generation systems have to solve this issue in order to obtain the users' acceptance. Therefore, there is a need for mechanisms or tools to protect and preserve people's privacy. This paper seeks to clarify how privacy can be protected in imagery data, so as a main contribution a comprehensive classification of the protection methods for visual privacy as well as an up-to-date review of them are provided. A survey of the existing privacy-aware intelligent monitoring systems and a valuable discussion of important aspects of visual privacy are also provided.

https://eprints.kingston.ac.uk/31628/1/Florez-Revuelta-F-31628.pdf

Visual privacy protection methods

Visual sensor networks (VSNs) are receiving a lot of attention in research, and at the same time, commercial applications are starting to emerge. VSN devices come with image sensors, adequate processing power, and memory. They use wireless communication interfaces to collaborate and jointly solve tasks such as tracking persons within the network. VSNs are expected to replace not only many traditional, closed-circuit surveillance systems but also to enable emerging applications in scenarios such as elderly care, home monitoring, or entertainment. In all of these applications, VSNs monitor a potentially large group of people and record sensitive image data that might contain identities of persons, their behavior, interaction patterns, or personal preferences. These intimate details can be easily abused, for example, to derive personal profiles. The highly sensitive nature of images makes security and privacy in VSNs even more important than in most other sensor and data networks. However, the direct use of security techniques developed for related domains might be misleading due to the different requirements and design challenges. This is especially true for aspects such as data confidentiality and privacy protection against insiders, generating awareness among monitored people, and giving trustworthy feedback about recorded personal data—all of these aspects go beyond what is typically required in other applications. In this survey, we present an overview of the characteristics of VSN applications, the involved security threats and attack scenarios, and the major security challenges. A central contribution of this survey is our classification of VSN security aspects into data-centric, node-centric, network-centric, and user-centric security. We identify and discuss the individual security requirements and present a profound overview of related work for each class. We then discuss privacy protection techniques and identify recent trends in VSN security and privacy. A discussion of open research issues concludes this survey.

Security and Privacy Protection in Visual Sensor Networks: A Survey

This paper presents a novel method for the protection of bitstreams of state-of-the-art video codec H.264/AVC. The problem of selective encryption (SE) is addressed along with the compression in the entropy coding modules. H.264/AVC supports two types of entropy coding modules. Context-adaptive variable length coding (CAVLC) is supported in H.264/AVC baseline profile and context-adaptive binary arithmetic coding (CABAC) is supported in H.264/AVC main profile. SE is performed in both types of entropy coding modules of this video codec. For this purpose, in this paper the encryption step is done simultaneously with the entropy coding CAVLC or CABAC. SE is performed by using the advanced encryption standard (AES) algorithm with the cipher feedback mode on a subset of codewords/binstrings. For CAVLC, SE is performed on equal length codewords from a specific variable length coding table. In case of CABAC, it is done on equal length binstrings. In our scheme, entropy coding module serves the purpose of encryption cipher without affecting the coding efficiency of H.264/AVC by keeping exactly the same bitrate, generating completely compliant bitstream and utilizing negligible computational power. Owing to no escalation in bitrate, our encryption algorithm is better suited for real-time multimedia streaming over heterogeneous networks. It is perfect for playback on handheld devices because of negligible increase in processing power. Nine different benchmark video sequences containing different combinations of motion, texture, and objects are used for experimental evaluation of the proposed algorithm.

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00603198/document

Fast Protection of H.264/AVC by Selective Encryption of CAVLC and CABAC for I and P Frames

The invention belongs to the technical field of digital image processing and mode identification, and specifically relates to an FPGA-based deep convolution neural network realizing method The hardware platform for realizing the method is XilinxZYNQ-7030 programmable sheet SoC, and an FPGA and an ARM Cortex A9 processor are built in the hardware platform Trained network model parameters are loaded to an FPGA end, pretreatment for input data is conducted at an ARM end, and the result is transmitted to the FPGA end Convolution calculation and down-sampling of a deep convolution neural network are realized at the FPGA end to form data characteristic vectors and transmit the data characteristic vectors to the ARM end, thus completing characteristic classification calculation Rapid parallel processing and extremely low-power high-performance calculation characteristics of FPGA are utilized to realize convolution calculation which has the highest complexity in a deep convolution neural network model The algorithm efficiency is greatly improved, and the power consumption is reduced while ensuring algorithm correct rate

FPGA-based deep convolution neural network realizing method

We present an optimum camera placement algorithm. We are motivated by the fact that the installation of security cameras is increasing rapidly. From the system cost point of view, it is desirable to observe all the area of interest by the smallest number of cameras. We propose a method for deciding optimum camera placement automatically considering camera specification such as visual distance, visual angle, and resolution. Moreover, to reduce the number of cameras, we divide the scene into regions and weight them according to their importance of observation. Then, we calculate camera locations which cover all the important regions and as many other regions as possible by considering the trade off between a number of observed regions and the number of cameras required. We formulate this problem as a set covering problem.

Optimum camera placement considering camera specification for security monitoring

Deep Neural Networks (DNNs) have progressed significantly in recent years. Novel DNN methods allow tasks such as image and speech recognition to be conducted easily and efficiently, compared with previous methods that needed to search for valid feature values or algorithms. However, DNN computations typically consume a significant amount of time and high-performance computing resources. To facilitate high-speed object recognition, this article introduces a Deep Convolutional Neural Network (DCNN) accelerator based on a field-programmable gate array (FPGA). Our hardware takes full advantage of the characteristics of convolutional calculation; this allowed us to implement all DCNN layers, from image input to classification, in a single chip. In particular, the dateflow from input to classification is uninterrupted and paralleled. As a result, our implementation achieved a speed of 409.62 giga-operations per second (GOPS), which is approximately twice as fast as the latest reported result. Furthermore, we used the same architecture to implement a Recurrent Convolutional Neural Network (RCNN), which can, in theory, provide better recognition accuracy.

A multistage dataflow implementation of a Deep Convolutional Neural Network based on FPGA for high-speed object recognition

Owing to the rapid progress in digital camera technologies, a large amount of image content is distributed on the World Wide Web. Digital camera identification, which is the identification of the source camera of an input image, is becoming increasingly important for presenting evidence in a court and helping police investigations. In recent years, a digital camera identification method using the image sensor's pattern noise has received considerable attention. Photo-response non-uniformity (PRNU) noise is mainly generated by the existence of differences between the sensitivities of pixels, and it is useful as a fingerprint of a camera. However, the PRNU noise of an image is usually contaminated by random noise and scene content and affected by the image processing engine, which inhibits stable identification. In this paper, we propose a novel digital camera identification method using the pairwise magnitude relations of image sensor noise, which are robust to noise contamination. By performing experiments, we demonstrate that the proposed method can identify the source cameras of query images with high accuracy.

Robust Digital Camera Identification Based on Pairwise Magnitude Relations of Clustered Sensor Pattern Noise

Video surveillance systems are becoming increasingly important for crime investigation and deterrence, and the number of cameras installed in public space is increasing. However, many cameras installed at fixed positions are required to observe a wide and complex area. In order to efficiently observe such a wide area at lower cost, mobile robots are an attractive option. In this paper, we propose a method for determining the traveling route of a mobile surveillance camera. Our method is based on mixed integer linear programming and obtains an optimum traveling route such that a camera with a certain visual angle and visual distance can observe the entire region at the shortest intervals. Through our experiments, we apply this method to several artificially generated data and data for a real university campus and demonstrate that effective patrol courses for specified mobile surveillance cameras can be generated.

Generation of an Optimum Patrol Course for Mobile Surveillance Camera

We present a novel framework for encoding images obtained by a security monitoring camera with protecting the privacy of moving objects in the images. We are motivated by the fact that although security monitoring cameras can deter crimes, they may infringe the privacy of those who and objects which are recorded by the cameras. Moving objects, whose privacy should be protected, in an input image (recorded by a monitoring camera) are encrypted and hidden in a JPEG bitstream. Therefore, a normal JPEG viewer generates a masked image, where the moving objects are unrecognizable or completely invisible. Only a special viewer with a password can reconstruct the original recording. Data hiding is achieved by watermarking and encrypting with the advanced encryption standard (AES). We illustrate a concept of our framework and an algorithm of the encoder and the special viewer. Moreover, we show an implementation example.

Hitoshi Kitazawa

Papers

Optimum camera placement considering camera specification for security monitoring

A multistage dataflow implementation of a Deep Convolutional Neural Network based on FPGA for high-speed object recognition

Robust Digital Camera Identification Based on Pairwise Magnitude Relations of Clustered Sensor Pattern Noise

Generation of an Optimum Patrol Course for Mobile Surveillance Camera

A new concept of security camera monitoring with privacy protection by masking moving objects