A Deep Genetic Programming Based Methodology for Art Media Classification Robust to Adversarial Perturbations

Olague, Gustavo; Ibarra-Vazquez, Gerardo; Chan-Ley, Mariana; Puente, Cesar; Soubervielle‐Montalvo, Carlos; Martinez, Axel

doi:10.1007/978-3-030-64556-4_6

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…This can help save time, whenever parameters of GP are needed to be set for any image processing related application. In literature, most of the reported work related to GP is oriented towards classification and object detection tasks. Relatively less work has been reported for image enhancement, registration, and compression, so more interesting techniques related to these fields can be exploited. Due to the heavy processing involved in image processing tasks, the algorithms require large training time. Training time can be considerably reduced by harnessing GPUs for enhanced algorithms. A GP‐based ensemble is likely to better exploit the decision spaces of the individual classifiers. Recently, deep neural networks have shown remarkable performance in many image processing applications 145,146 . In this regard, the meta classification/regression of individual learners, and specifically that of deep neural networks through GP, has good potential in learning complex problems 147 …”

Section: Discussionmentioning

confidence: 99%

A recent survey on the applications of genetic programming in image processing

Khan

Qureshi

Wahab

et al. 2021

Computational Intelligence

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Genetic programming (GP) has been primarily used to tackle optimization, classification, and feature selection related tasks. The widespread use of GP is due to its flexible and comprehensible tree‐type structure. Similarly, research is also gaining momentum in the field of image processing, because of its promising results over vast areas of applications ranging from medical image processing to multispectral imaging. Image processing is mainly involved in applications such as computer vision, pattern recognition, image compression, storage, and medical diagnostics. This universal nature of images and their associated algorithm, that is, complexities, gave an impetus to the exploration of GP. GP has thus been used in different ways for image processing since its inception. Many interesting GP techniques have been developed and employed in the field of image processing, and consequently, we aim to provide the research community an extensive view of these techniques. This survey thus presents the diverse applications of GP in image processing and provides useful resources for further research. In addition, the comparison of different parameters used in different applications of image processing is summarized in tabular form. Moreover, analysis of the different parameters used in image processing related tasks is carried‐out to save the time needed in the future for evaluating the parameters of GP. As more advancement is made in GP methodologies, its success in solving complex tasks, not only in image processing but also in other fields, may increase. In addition, guidelines are provided for applying GP in image processing related tasks, the pros and cons of GP techniques are discussed, and some future directions are also set.

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Section: Discussionmentioning

confidence: 99%

A recent survey on the applications of genetic programming in image processing

Khan

Qureshi

Wahab

et al. 2021

Computational Intelligence

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“…This paper provides insight into adversarial attacks and the motivation to analyze image classification models' robustness. Therefore, we extend the first results reported at the International Symposium on Visual Computing (ISVC '20), on which we explore the robustness through the complex image classification task of the AMC [28]. In this work, we test a prevailing BoV approach from CV, four state-of-the-art DCNN models (AlexNet, VGG, ResNet, ResNet101), and the BP algorithm using three AAs (Fast Gradient Sign Method-FGSM, multiple pixel attack, and adversarial patch).…”

Section: Research Contributionsmentioning

confidence: 91%

Brain Programming is Immune to Adversarial Attacks: Towards Accurate and Robust Image Classification using Symbolic Learning

Ibarra-Vazquez,

Olague,

Chan-Ley

et al. 2021

Preprint

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gories using adversarial patches without changes and for the remaining three classes with a variation of 1%. Additionally, the statistical analysis showed that the predictions' confidence of BP were not significantly different for each pair of clean and perturbed images in every experiment. These results prove BP's robustness against adversarial examples compared to DCNN and handcrafted features methods, whose performance on the art media classification was compromised with the proposed perturbations. We also ratify the competitive score of BP against the state-of-the-art classifiers for the art media categorization problem.

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“…Another challenge in computer vision is that of adversarial attacks where an input image is minimally manipulated causing the system to miss-classify objects. A Deep Genetic Programming method called Brain Programming was introduced by Olague et al [35] that is robust to adversarial attacks in comparison to the convolutional AlexNet model when compared on two artworks databases.…”

Section: Alternative Architecturesmentioning

confidence: 99%

Leaf Disease Segmentation and Detection in Apple Orchards for Precise Smart Spraying in Sustainable Agriculture

Storey

Meng

2022

Sustainability

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Reduction in chemical usage for crop management due to the environmental and health issues is a key area in achieving sustainable agricultural practices. One area in which this can be achieved is through the development of intelligent spraying systems which can identify the target for example crop disease or weeds allowing for precise spraying reducing chemical usage. Artificial intelligence and computer vision has the potential to be applied for the precise detection and classification of crops. In this paper, a study is presented that uses instance segmentation for the task of leaf and rust disease detection in apple orchards using Mask R-CNN. Three different Mask R-CNN network backbones (ResNet-50, MobileNetV3-Large and MobileNetV3-Large-Mobile) are trained and evaluated for the tasks of object detection, segmentation and disease detection. Segmentation masks on a subset of the Plant Pathology Challenge 2020 database are annotated by the authors, and these are used for the training and evaluation of the proposed Mask R-CNN based models. The study highlights that a Mask R-CNN model with a ResNet-50 backbone provides good accuracy for the task, particularly in the detection of very small rust disease objects on the leaves.

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A Deep Genetic Programming Based Methodology for Art Media Classification Robust to Adversarial Perturbations

Cited by 5 publications

References 13 publications

A recent survey on the applications of genetic programming in image processing

A recent survey on the applications of genetic programming in image processing

Brain Programming is Immune to Adversarial Attacks: Towards Accurate and Robust Image Classification using Symbolic Learning

Leaf Disease Segmentation and Detection in Apple Orchards for Precise Smart Spraying in Sustainable Agriculture

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