Abstract
Even when deep convolutional neural networks have proven to be effective at saliency detection, they have a vulnerability that should not be ignored: they are susceptible to adversarial attacks, making them highly unreliable. Reliability is an important aspect to consider when it comes to salient object detection; without it, an attacker can render the algorithm useless. Brain programming–an evolutionary methodology for visual problems–is highly resilient and can withstand even the most intense perturbations. In this work, we perform for the first time a study that compares the resilience against adversarial attacks and noise perturbations using a real-world database of a shorebird called the Snowy Plover in a visual attention task. Database images were taken on the field and even posed a detection challenge due to the nature of the environment and the bird’s physical characteristics. By attacking three different deep convolutional neural networks using adversarial examples from this database, we prove that they are no match for the brain programming algorithm when it comes to resilience, suffering significant losses in their performance. On the other hand, brain programming stands its ground and sees its performance unaffected. Also, by using images of the Snowy Plover, we refer to the importance of resilience in real-world issues where conservation is present. Brain programming is the first highly resilient evolutionary algorithm used for saliency detection tasks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Athalye, A., Engstrom, L., Ilyas, A., Kwok, K.: Synthesizing robust adversarial examples (2018)
Brown, T.B., Mané, D., Roy, A., Abadi, M., Gilmer, J.: Adversarial patch (2018)
Doull, K., Chalmers, C., Fergus, P., Longmore, S., Piel, A., Wich, S.: An evaluation of the factors affecting ‘poacher’ detection with drones and the efficacy of machine-learning for detection. Sensors 21(12), 4074 (2021). https://doi.org/10.3390/s21124074
Dozal, L., Olague, G., Clemente, E., Hernandez, D.: Brain programming for the evolution of an artificial dorsal stream. Cogn. Comput. 6(3), 528–557 (2014)
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015)
Hubel, D., Wiesel, T.: Receptive fields of single Neurones in the cat’s striate cortex. J. Physiol. 148(3), 574–591 (1953)
Kahl, S., Wood, C.M., Eibl, M., Klinck, H.: BirdNet: a deep learning solution for avian diversity monitoring. Ecol. Inform. 61, 101236 (2021). https://doi.org/10.1016/j.ecoinf.2021.101236, https://www.sciencedirect.com/science/article/pii/S1574954121000273
Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Humam Neurobiol. 4, 219–227 (1985)
Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, 1st edn. A Bradford Book, Cambridge (1992)
Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks, pp. 1106–1114 (2012)
Kurakin, A., Goodfellow, I.J., Bengio, S.: Adversarial examples in the physical world (2016)
Liu, N., Han, J., Yang, M.H.: PicaNet: learning pixel-wise contextual attention for saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
Norouzzadeh, M., et al.: Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci. United States Am. 115(25), E5716–E5725 (2018). https://doi.org/10.1073/pnas.1719367115
Olague, G., Clemente, E., Dozal, L., Hernandez, D.: Evolving an artificial visual cortex for object recognition with brain programming. In: Schuetze, O. et al. (eds.) EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III. Studies in Computational Intelligence, vol. 500, pp. 97–119. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-319-01460-9_5
Oram, M., Perrett, D.: Modeling visual recognition from neurobiological constraints. Neural Netw. 7(6), 945–972 (1994)
Rensink, R.: Seeing, sensing and scrutinizing. Vis. Res. 40(10–12), 1469–1487 (2000)
Su, J., Vargas, D.V., Sakurai, K.: One pixel attack for fooling deep neural networks. IEEE Trans. Evol. Comput. 23(5), 828–841 (2019). https://doi.org/10.1109/tevc.2019.2890858, http://dx.doi.org/10.1109/TEVC.2019.2890858
Szegedy, C., et al.: Intriguing properties of neural networks (2014)
Treisman, A., Gelade, G.: A feature-integration theory of attention. Cogn. Psychol. 12(1), 97–136 (1980)
Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Netw. 19(9), 1395–1407 (2006)
Wang, W., Lai, Q., Fu, H., Shen, J., Ling, H., Yang, R.: Salient object detection in the deep learning era: an in-depth survey. IEEE Trans. Patt. Anal. Mach. Intell. (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Pineda, R., Olague, G., Ibarra-Vazquez, G., Martinez, A., Vargas, J., Reducindo, I. (2022). Brain Programming and Its Resilience Using a Real-World Database of a Snowy Plover Shorebird. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_38
Download citation
DOI: https://doi.org/10.1007/978-3-031-02462-7_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-02461-0
Online ISBN: 978-3-031-02462-7
eBook Packages: Computer ScienceComputer Science (R0)