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Counterfeit integrated circuits (ICs) constitute a major threat to system reliability, as well as security from personal to national scale. Most current techniques for counterfeit electronics detection are expensive, time consuming, and prone to human error. We have proposed a convolutional neural network (CNN) architecture–based supervised technique along with two unsupervised techniques based on...
The widespread penetration of counterfeit integrated circuits (ICs) is not only a major threat to the electronic goods supply chain, but also constitute a great threat to national security. Image processing based counterfeit IC design techniques are promising, but currently often suffer from high computational complexity and requirement of expensive image acquisition infrastructure. We describe two...
Image content or metadata editing software availability and ease of use has resulted in a high demand for automatic image tamper detection algorithms. Most previous work has focused on detection of tampered image content, whereas we develop techniques to detect metadata tampering in outdoor images using sun altitude angle and other meteorological information like temperature, humidity and weather,...
Depth estimation using stereoscopy can be done by comparing the relative positions of the same (point) object displayed in the two different images captured by two cameras. Quantization errors that follow a square law with respect to distance are one of the errors that arise during depth estimation. The solution to limit this error is to use a multi camera arrangement. We deal with the physical aspect...
We present here our approach to the problem of improving the efficiency of stereo depth map computation. The algorithm is applied on rectified images. Graph cut is used for energy minimization. The descriptors used are both SIFT and DAISY. This algorithm produces fast results of approximate disparity maps from two images. The main advantage of our algorithm is its efficiency and reduction of computation...
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