Copy sensitive graphical codes are used as anti-counterfeiting solution in packaging and document protection. Their security is funded on a design hard-to-predict after print and scan. In practice there exist different designs. Here random codes printed at the printer resolution are considered. We suggest an estimation of such codes by using neural networks, an in-trend approach which has however not been studied yet in the present context. In this paper, we test a state-of-the-art architecture efficient in the binarization of handwritten characters. The results show that such an approach can be successfully used by an attacker to provide a valid counterfeited code so fool an authentication system. CCS CONCEPTS • Security and privacy → Authentication; • Computing methodologies → Neural networks.
Dental and Oral diseases are very common diseases and half of the world population suffers from it. Due to poverty or unhygienic practices, these diseases are common, and it is estimated that 5% of total medical expenditure in the world is on oral diseases. In this paper, we have focused on detecting cavities. Recent developments Machine Learning and Artificial Intelligence have helped a lot in medical science. Due to these algorithms, diagnosis and treatment of diseases can be done efficiently. To detect dental cavities different imaging modalities are used by doctors, however, in this paper we have used visual images of teeth’s and applied deep convolution neural network(CNN) to classify the teeth into caries or non-caries. We have used the images from the Kaggle dataset, and after tuning our model we were able to achieve 71.43% accuracy.
Recent papers point out the vulnerability of Copy Sensitive Graphical Codes (CSGC) while an opponent uses a neural network approach to estimate a pattern then prints it as an original one: such a fake can successfully pass the authentication test. Here, we show that a GAN-like network can be even more powerful. A SRGAN-based architecture including superresolution can tolerate a lower scanner resolution and decode efficiently. Besides, the use of such a decoding technique to perform the authentication test can improve the resistance of CSGC to estimation attacks.
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