Название: Computer Vision and Machine Intelligence: Proceedings of CVMI 2022Автор: Massimo Tistarelli, Shiv Ram Dubey, Satish Kumar Singh
Издательство: Springer
Год: 2023
Страниц: 777
Язык: английский
Формат: pdf (true)
Размер: 21.1 MB
This book presents selected research papers on current developments in the fields of computer vision and machine intelligence from International Conference on Computer Vision and Machine Intelligence (CVMI 2022). The book covers topics in image processing, Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, machine intelligence, etc. The book is useful for researchers, postgraduate and undergraduate students, and professionals working in this domain.
The decomposition of 3D shapes into simple yet representative components is a very intriguing topic in Computer Vision as it is very useful for many possible applications. Superquadrics may be used with benefit to obtain an implicit representation of the 3D shapes, as they allow to represent a wide range of possible forms by few parameters. However, in the computation of the shape representation, there is often an intricate trade-off between the variation of the represented geometric forms and the accuracy in such implicit approaches. In this paper, we propose an improved loss function, and we introduce beneficial computational techniques. By comparing results obtained by our new technique to the baseline method, we demonstrate that our results are more reliable and accurate, as well as much faster to obtain.
Adversarial Machine Learning is an emerging area showing the vulnerability of Deep Learning models. Exploring attack methods to challenge state-of-the-art Artificial Intelligence (AI) models is an area of critical concern. The reliability and robustness of such AI models are one of the major concerns with an increasing number of effective adversarial attack methods. Classification tasks are a major vulnerable area for adversarial attacks. The majority of attack strategies are developed for colored or gray-scaled images. Consequently, adversarial attacks on binary image recognition systems have not been sufficiently studied. Binary images are simpletwo possible pixel-valued signals with a single channel. The simplicity of binary images has a significant advantage compared to colored and gray-scaled images, namely computation efficiency. Moreover, most optical character recognition systems (OCRs), such as handwritten character recognition, plate number identification, and bank check recognition systems, use binary images or binarization in their processing steps. In this paper, we propose a simple yet efficient attack method, efficient combinatorial black-box adversarial attack (ECoBA), on binary image classifiers. We validate the efficiency of the attack technique on two different data sets and three classification networks, demonstrating its performance. Furthermore, we compare our proposed method with state-of-the-art methods regarding advantages and disadvantages as well as applicability.
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