Dr. Hans C. Palm Profile

Dr. Hans C. Palm

Principal Scientist at Norwegian Defence Research Establishment

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Publications (9)

Proceedings Article | 20 September 2020 Paper

Investigating robustness of adversarial camouflage (AC) for naval vessels

Kristin Hammarstrøm Løkken, Lars Aurdal, Alvin Brattli, Hans Christian Palm

Proceedings Volume 11543, 115430G (2020) https://doi.org/10.1117/12.2573676

KEYWORDS: Camouflage, Gallium nitride, Neural networks, Visualization

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The use of camouflage is widespread in the biological domain, and has also been used extensively by armed forces around the world in order to make visual detection and classification of objects of military interest more difficult. The recent advent of ever more autonomous military agents raises the questions of whether camouflage can have a similar effect on autonomous agents as it has on human agents, and if so, what kind of camouflage will be effective against such adversaries. In previous works, we have shown that image classifiers based on deep neural networks can be confused by patterns generated by generative adversarial networks (GANs). Specifically, we trained a classifier to distinguish between two ship types, military and civilian. We then used a GAN to generate patterns that, when overlaid on parts of military vessels (frigates), made the classifier confuse the modified frigates with civilian vessels. We termed such patterns "adversarial camouflage" (AC) since these patterns effectively camouflage the frigates with respect to the classifier. The type of adversarial attack described in our previous work is a so-called white box attack. This term describes adversarial attacks that are devised given full knowledge of the classifier under attack. This is as opposed to black box attacks, which describe attacks on unknown classifiers. In our context, the ultimate goal is to design a GAN that is capable of black box attacks, in other words: a GAN that will generate AC that has effect across a wide range of neural network classifiers. In the current work, we study techniques to improve the robustness of our GAN-based approach by investigating whether a GAN can be trained to fool a selection of several neural network-based classifiers, or reduce the confidence of the classifications to a degree which makes them unreliable. Our results indicate that it is indeed possible to weaken a wider range of neural network classifiers by training the generator on several classifiers.

Proceedings Article | 24 April 2020 Paper

Robustness of adversarial camouflage (AC) for naval vessels

Kristin Hammarstrøm Løkken, Alvin Brattli, Hans Christian Palm, Lars Aurdal, Runhild Aae Klausen

Proceedings Volume 11394, 113940W (2020) https://doi.org/10.1117/12.2558506

KEYWORDS: Camouflage, Neural networks, Image filtering, Evolutionary algorithms, Optical sensors

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Proceedings Article | 22 April 2020 Paper

Classification based on fast and robust approximations to order statistics

Hans Christian Palm

Proceedings Volume 11400, 114000I (2020) https://doi.org/10.1117/12.2558502

KEYWORDS: Image segmentation, Contamination, Statistical analysis, Infrared imaging, Cameras, Imaging systems, Automatic target recognition

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Proceedings Article | 19 September 2019 Presentation + Paper

Adversarial camouflage for naval vessels

Lars Aurdal, Kristin Hammarstrøm Løkken, Runhild Aae Klausen, Alvin Brattli, Hans Christian Palm

Proceedings Volume 11169, 111690K (2019) https://doi.org/10.1117/12.2532756

KEYWORDS: Camouflage, Neural networks, Image analysis, Detection and tracking algorithms, Sensors, Network architectures, Evolutionary algorithms, Visualization, Artificial intelligence

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Proceedings Article | 9 October 2018 Paper

Supporting artificial intelligence with artificial images

Lars Aurdal, Alvin Brattli, Eirik Glimsdal, Runhild Aae Klausen, Kristin Hammarstrøm Løkken, Hans Christian Palm

Proceedings Volume 10794, 107940M (2018) https://doi.org/10.1117/12.2324969

KEYWORDS: Infrared imaging, Long wavelength infrared, Neural networks, Image processing, Image segmentation, Infrared signatures, Network architectures

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Proceedings Article | 30 April 2018 Paper

Fast and robust image processing algorithms based on approximations to order statistics

H. Palm

Proceedings Volume 10648, 1064807 (2018) https://doi.org/10.1117/12.2304336

KEYWORDS: Image segmentation, Visualization, Statistical analysis, Image processing algorithms and systems, Image processing, Algorithm development, Infrared imaging

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Proceedings Article | 3 June 2013 Paper

An ATR architecture for algorithm development and testing

Gøril Breivik, Kristin Løkken, Alvin Brattli, Hans Palm, Trym Haavardsholm

Proceedings Volume 8744, 874402 (2013) https://doi.org/10.1117/12.2015369

KEYWORDS: Image segmentation, Cameras, Automatic target recognition, Algorithm development, Image processing algorithms and systems, Image processing, Infrared radiation, Detection and tracking algorithms, Long wavelength infrared, Infrared cameras

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Proceedings Article | 2 May 2013 Paper

Extraction and classification of vehicles in LADAR imagery

Hans Palm, Trym Haavardsholm, Halvor Ajer, Cathrine Jensen

Proceedings Volume 8731, 873102 (2013) https://doi.org/10.1117/12.2015363

KEYWORDS: Sensors, Image segmentation, LIDAR, Statistical analysis, Digital filtering, Image filtering, Distance measurement, 3D modeling, Model-based design, Buildings

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Proceedings Article | 18 September 2003 Paper

Imaging ground sensor system OPAK

Stein Grinaker, Hans Palm

Proceedings Volume 5090, (2003) https://doi.org/10.1117/12.500707

KEYWORDS: Imaging systems, Optical sensors, Image sensors, Sensors, Unattended ground sensors, Technologies and applications, Computer intrusion detection

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Showing 5 of 9 publications

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