ABSTRACT Deep learning (DL) has significantly transformed computer vision, demonstrating remarkable achievements and extensive real‐world applications. However, recent studies have highlighted a critical vulnerability of DL models to adversarial examples (AE), where slight perturbations in input data can lead to erroneous outputs. We observe that the behaviour of the AE is similar to a chaotic system, where a minor change in the input leads to a significantly different output. In response, we propose a novel approach for detecting and categorizing adversarial inputs encountered by classification neural networks. The proposed approach focuses on extracting statistical profiles, termed as chaotic feature vectors (CFVs), from a collection of features derived from ordinal patterns (OP). In this work, the proposed AE detection method is tested on seven attack methods and three image datasets including MNIST, FMNIST and CIFAR10. The results indicate that CFVs exhibit promising capabilities in discerning AE against various types of adversarial attacks on different datasets. This advancement lays the foundation for devising attack mitigation strategies, thereby enhancing the robustness and security of DL models in the face of adversarial threats.

ABSTRACT Adversarial examples are an intriguing and critical topic in the field of machine learning. The impact of malignant perturbations on deep learning‐based systems, especially in safety‐critical applications, highlights a significant security concern. While most research has focused on artificially generated adversarial attacks–crafted through optimization algorithms and constrained perturbations, it is important to note that adversarial examples can also occur naturally, without any artificial manipulation, during the prediction of real‐world images. These naturally occurring adversarial examples pose unique challenges, as they are harder to detect and interpret. Despite their importance, the study of natural adversarial examples remains in its early stages. Fundamental questions remain unanswered: Do natural adversarial examples exhibit similar behaviours or properties as artificially generated ones? How should models be adapted to improve their robustness against such natural inputs? To address these questions, this work proposes an in‐depth analysis of activation maps to compare the internal behaviour of neural networks when processing clean images, artificially perturbed inputs and natural adversarial examples. A set of quantitative metrics is extracted from activation heatmaps at various network layers, including mean activation intensity, centroid displacement and standard reference image quality metrics. These measurements enable a systematic comparison of how the network attends to different image regions under varying conditions. The experimental results demonstrate that natural adversarial examples exhibit statistically significant differences in activation patterns compared to their artificial counterparts, suggesting that they may require distinct strategies for detection and defence.

Abstract The phenomenon of adversarial examples is one of the most attractive topics in machine learning research these days. These are particular cases that are able to mislead neural networks, with critical consequences. For this reason, different approaches are considered to tackle the problem. On the one side, defense mechanisms, such as AutoEncoder-based methods, are able to learn from the distribution of adversarial perturbations to detect them. On the other side, chaos theory and Lyapunov exponents (LEs) have also been shown to be useful to characterize them. This work proposes the combination of both domains. The proposed method employs these exponents to add more information to the loss function that is used during an AutoEncoder training process. As a result, this method achieves a general improvement in adversarial examples detection performance for a wide variety of attack methods.

Soil-transmitted helminth infections are one of the most common healthcare problems worldwide and they especially affect to the poorest communities in tropical and subtropical areas. Nowadays, diagnosis of intestinal parasites is performed by highly skilled medical staff, directly examining samples in the laboratory via a microscope, a laborious and time-consuming work. Automatic deep learning-based object detection methods can help to automatically detect and identify intestinal parasitic eggs, or at least reduce the workload. In this work, the application of novel Transformer-based architectures is proposed to solve the parasitic egg detection task in microscopic images. Several detection methods and backbones have been analyzed and compared, obtaining up to 0.875 mIoU score on the dataset used for testing.

Intestinal parasitic infections are a healthcare problem with a high impact in some areas. While nowadays the assessment performed by experts is mostly manual, it is possible to introduce machine learning techniques to help automating this task, or at least reduce the workload. This can lead to shorter detection times and faster treatment application. In the context of deep learning, several object detection techniques have been proposed and validated on general purpose datasets such as ImageNet or COCO. In this work, an ensemble of these is proposed for this particular task. The merged detections of FasterRCNN, TOOD, YOLOX and Cascade with Swin-Transformers applied to this problem achieved 0.915 IoU, larger than the result that each method obtained independently.

Automating the analysis of soil parameters can optimize the fertilization process, saving time and reducing the costs of food production, leading to a more sustainable agriculture. The work presented in this paper is part of the HYPERVIEW Challenge: Seeing Beyond the Visible. Several methods are proposed, based both on traditional approaches such as Support Vector Regression (SVR) and k-Nearest Neighbors (k-NN), as well as modern neural networks. A parameterized preprocessing stage has been proposed to deal with the varying size of the input data. The best results have been obtained with the k-NN model and the grid division of the images.

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.