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DOI: 10.14489/vkit.2020.02.pp.032-038
Сакулин С. А., Алфимцев А. Н., Локтев Д. А., Коваленко А. О., Девятков В. В.
ЗАЩИТА ИЗОБРАЖЕНИЯ ЧЕЛОВЕКА ОТ РАСПОЗНАВАНИЯ НЕЙРОСЕТЕВОЙ СИСТЕМОЙ НА ОСНОВЕ СОСТЯЗАТЕЛЬНЫХ ПРИМЕРОВ
(c. 32-38)
Аннотация. Предложен метод конструирования наносимого на одежду камуфляжа специально подобранного типа, который защищает человека как от распознавания человеком-наблюдателем, так и от глубокой нейросетевой системы распознавания. Такой тип камуфляжа сконструирован на основе состязательных примеров, которые генерируются с помощью глубокой нейросети. Дано описание экспериментов по защите человека от распознавания системами Faster-RCNN (Regional Convolution Neural Networks) Inception V2 и Faster-RCNN ResNet101. Результаты показали высокую эффективность предложенного метода в виртуальном мире, когда имеется доступ к каждому пикселу подаваемого на вход системы изображения. В реальном мире результаты менее внушительные, что может быть объяснено искажениями цветов при печати на ткани, а также недостаточным пространственным разрешением такой печати.
Ключевые слова: распознавание человека; глубокое обучение; состязательные примеры; глубокие нейросети.
Sakulin S. A., Alfimtsev A. N., Loktev D. A., Kovalenko A. O., Devyatkov V. V.
USAGE OF ADVERSARIAL EXAMPLES TO PROTECT A HUMAN IMAGE FROM BEING DETECTED BY RECOGNITION SYSTEMS BASED ON DEEP NEURAL NETWORKS
(pp. 32-38)
Abstract. Recently, human recognition systems based on deep machine learning, in particular, on the basis of deep neural networks, have become widespread. In this regard, research has become relevant in the field of protection against recognition by such systems. In this article a method of designing a specially selected type of camouflage applied to clothing, which will protect a person both from recognition by a human observer and from a deep neural network recognition system is proposed. This type of camouflage is constructed on the basis of competitive examples that are generated by a deep neural network. The article describes experiments on human protection from recognition by Faster-RCNN (Regional Convolution Neural Networks) Inception V2 and Faster-RCNN ResNet101 systems. However, the implementation of camouflage is considered on a macro level, which assesses the combination of the camouflage and background, and the micro level which analyzes the relationship between the properties of individual regions of the camouflage properties of the adjacent regions, with constraints on their continuity, smoothness, closure, asymmetry. The dependence of camouflage characteristics on the conditions of observation of the object and the environment is also considered: the transparency of the atmosphere, the intensity of pixels of the sky horizon and the background, the level of contrast of the background and the camouflaged object, the distance to the object. As an example of a possible attack, a “black box” attack, which involves preliminary testing of generated adversarial examples on a target recognition system without knowledge of the internal structure of this system, is considered. Results of these experiments showed the high efficiency of the proposed method in the virtual world, when there is access to each pixel of the image supplied to the input systems. In the real world, results are less impressive, which can be explained by the distortion of colors when printing on the fabric, as well as the lack of spatial resolution of this print.
Keywords: Human detection; Deep learning; Adversarial examples; Deep neural networks.
С. А. Сакулин, А. Н. Алфимцев, Д. А. Локтев, А. О. Коваленко, В. В. Девятков (Московский государственный технический университет им. Н. Э. Баумана, Москва, Россия) E-mail:
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S. A. Sakulin, A. N. Alfimtsev, D. A. Loktev, A. O. Kovalenko, V. V. Devyatkov (Bauman Moscow State Technical University, Moscow, Russia) E-mail:
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7. Zheng Y. et al. (2018). Detection of People With Camouflage Pattern Via Dense Deconvolution Network. IEEE Signal Processing Letters, Vol. 26, (1), pp. 29 – 33.
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17. Mirza M., Osindero S. (2014). Conditional Generative Adversarial Nets. arXiv preprint arXiv:1411.1784. 2014.
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