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Vision-Based System Design Part 11 – Securing Embedded Vision Systems against Malicious Attackers


Using a system optimising compiler such as SDSoC™, enables the developer to specify the algorithm using a high-level language like C or C++. This enables development of the security solution at a higher level, and then acceleration of bottleneck functions into the programmable logic.
AES is a symmetrical algorithm which uses the same key for both encryption and decryption. The AES algorithm can be computationally intensive, requiring substitutions with a defined S Box, Matrix Multiplications and several shift operations. As such, implementing AES encryption or decryption in a CPU can become a processing bottleneck.

Implementing AES encryption using SDSoC enables a significant acceleration in the performance when accelerated into the programmable logic for each of the supported operating systems.

There is an increase in the number of threats which face the embedded vision developer. Performing a threat analysis can help identify the actual threats to the system, enabling the design team to create a design which negates the threat vectors. Devices like the Zynq-7000 and Zynq UltraScale+ MPSoC support security solutions with embedded silicon features and support for secure configuration. Run time solutions such as Trustzone and isolation, can be implemented along with creating encryption engines using SDSoC, which aligns with a reVISION development flow.

One of the advantages of embedded vision systems is their ability to observe wavelengths outside those which are visible to humans. This enables the embedded vision system to provide superior performance across a range of applications from low light vision to scientific imaging and analysis.
While imaging systems at higher wavelengths including X Ray and Ultraviolet are used for scientific applications such as astronomy, it is IR wavelengths which are most often deployed in industrial, automotive and security applications. As IR imagers sense the background thermal radiation, they require no scene illumination and provide the ability to see in total darkness, making them ideal for automotive and security applications, while within the industrial sphere, IR systems can also be used in thermo graphic applications where they accurately measure the temperature of the scene contents. For example, in renewable energy, thermal imagers can be combined with drones to monitor the performance of solar arrays and detect early failures due to the increasing temperature of failing elements.
Working outside the visible range requires the correct selection of the imaging device technology. If the system operates within the near-IR spectrum or below, developers can use devices such as Charge Coupled Devices (CCDs) or CMOS (Complementary Metal Oxide Semiconductor) Image Sensors (CIS). However, as developers move into the infrared spectrum they need to use specialized IR detectors.
The need for specialized sensors in the IR domain is in one part due to the excitation energy required for silicon based imagers such as CCD or CIS. These typically require photon energy of 1eV to excite an electron but at IR wavelengths photon energies range from 1.24 meV to 1.7eV. As such, IR imagers tend to be based upon HgCdTe or InSb. These have lower excitation energies and are often combined with a CMOS readout IC called a ROIC to control and readout the sensor.
IR systems fall into two categories, cooled and uncooled. Cooled thermal imagers use image sensor technology based upon HgCdTe or InSb semiconductors. To provide useful images a thermal imager requires the use of a cooling system to reduce the temperature of the sensor to 70 to 100 Kelvin. This is required to reduce the generated thermal noise to below that which is generated by the scene contents. Using a cooled sensor brings with it an increased complexity, cost and weight for the cooling system, the system also takes time (several minutes) to reach operating temperature and generate a useable picture.
Uncooled IR sensors can operate at room temperatures and use micro bolometers in place of an HgCdTe or InSb sensor. A micro bolometer works by each pixel changing resistance when IR radiation strikes it. This resistance change defines the temperatures in the scene. Typically, micro bolometer-based thermal imagers have much-reduced resolution when compared with a cooled imager. However, they do make thermal-imaging systems simpler, lighter and less costly to create.

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