In this paper we explore two different approaches for designing AI-generated image attribution methods that are robust in adversarial settings, namely adversarial training (AT) and randomized smoothing (RS). While AT has been widely adopted in machine learning to improve the adversarial robustness of classifiers, its application to source image attribution is still unexplored. RS, on the other hand, has emerged as a method for developing deep learning classifiers with certified robustness, i.e., for which a certified level of robustness can be theoretically guaranteed, regardless of the specific manipulation causing the distortion. With the exception of a single prior study, its application to forensic tasks has not been previously explored. Experiments conducted on two datasets of AI-generated images show that both approaches achieve substantial adversarial robustness and exhibit a general resistance to common image manipulations. In particular, adversarial training provides stronger robustness against a broad range of post-processing operations, whereas randomized smoothing yields higher practical robustness against adversarial attacks.

Molecule generation has experienced multiple breakthroughs in recent years. While traditional techniques are very reliable they have low space exploration potential; therefore, machine learning techniques are needed to delve deep into the vast space of potential compounds, helping to find new ways of designing candidate molecules. This paper presents a sequential Markovian model based on graph neural networks for molecular generation. The model employs a Breadth–First Search (BFS) ordering strategy and a modular architecture to enhance independence between functions, with a focus on maintaining strict independence at each step of the Markovian process. Experimental results on ZINC (MOSES), ZINC (250 K) and Polymers datasets demonstrate the model’s ability to perform both unconditional and conditional molecule generation while preserving dataset properties. Notably, the step–wise approach achieves state–of–the–art results in terms of uniqueness and validity without using valency masks. • A novel sequential approach to molecular graph generation is presented. • The method exploits Graph Neural Networks to maximize information exploitation. • The employed Markov process and the architecture ensure a flexible learning procedure. • Conditional generation allows tailoring the generated molecules to the objectives of the specific studies. • The experimentation shows that the approach outperforms thestate-of-the-art.

Graph generative models often lack a proper reconstruction loss to evaluate the distance between the generated graph and the target graph. This is particularly important for molecular graph generators based on autoencoders, which should reconstruct the input graphs as precisely as possible. Though, the distance estimation can be useful for any graph generator based on reconstruction, including sequential methods relying on Graph Neural Networks. Since graphs are discrete entities by nature, general graph spaces lack a reliable, general, and computationally affordable distance function. Graph Edit Distance is of course an exact, general, and permutation–invariant method for evaluating the difference between two graphs defined in the same graph space. Since it needs all the possible combinations of pairs of nodes from the two graphs, its exact computation is a NP-complete problem and cannot be carried out for graphs larger than ten nodes. As a consequence, a comprehensive soft–estimation method for Graph Edit Distance based on siamese Graph Neural Networks is proposed. A theoretical discussion is carried out, showing that the proposed method can provide a reliable and precise soft–estimation of the Graph Edit Distance on molecular graphs. Molecular graph generators can therefore use this distance estimation as a powerful non–permutation–invariant reconstruction loss. Moreover, the experimental results show that the distance estimation is accurate, with a very low Mean Squared Error loss value.

Artificial Intelligence (AI)–generated images represent a significant threat in various fields, such as security, privacy, media forensics and content moderation. In this paper, a novel approach for the detection of StyleGAN2–generated human faces is presented, leveraging a Transfer Learning strategy to improve the Classification performance of the models. A modified version of the state– of–the–art semantic segmentation model DeepLabV3+, using either a ResNet50 or a MobileNetV3 Large as feature extraction backbones, is used to create both a face segmentation model and the synthetic image detector. To achieve this goal, the models are at first trained for face segmentation in a multi–class Classification task on a widely used semantic segmentation dataset, achieving remarkable results for both configurations. Then, the pre–trained models are retrained on a collection of real and generated images, gathered from different sources to solve a binary Classification task, namely to detect synthetic (i.e. generated) images, thus carrying out two different transfer learning strategies. The results indicate that this targeted methodology significantly improves the detection rates compared to analyzing the face as a whole, and underlines the importance of advanced image recognition technologies when tackling the challenge of detecting generated faces.

Predicting drug side effects before they occur is a critical task for keeping the number of drug-related hospitalizations low and for improving drug discovery processes. Automatic predictors of side-effects generally are not able to process the structure of the drug, resulting in a loss of information. Graph neural networks have seen great success in recent years, thanks to their ability of exploiting the information conveyed by the graph structure and labels. These models have been used in a wide variety of biological applications, among which the prediction of drug side-effects on a large knowledge graph. Exploiting the molecular graph encoding the structure of the drug represents a novel approach, in which the problem is formulated as a multi-class multi-label graph-focused classification. We developed a methodology to carry out this task, using recurrent Graph Neural Networks, and building a dataset from freely accessible and well established data sources. The results show that our method has an improved classification capability, under many parameters and metrics, with respect to previously available predictors. The method is not ready for clinical tests yet, as the specificity is still below the preliminary 25% threshold. Future efforts will aim at improving this aspect.

Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side-effects, before their occurrence, is fundamental to reduce this impact, in particular on drug discovery. Candidate molecules could be screened before undergoing clinical trials, reducing the costs in time, money, and health of the participants. Drug side-effects are triggered by complex biological processes involving many different entities, from drug structures to protein-protein interactions. To predict their occurrence, it is necessary to integrate data from heterogeneous sources. In this work, such heterogeneous data is integrated into a graph dataset, expressively representing the relational information between different entities, such as drug molecules and genes. The relational nature of the dataset represents an important novelty for drug side-effect predictors. Graph Neural Networks (GNNs) are exploited to predict DSEs on our dataset with very promising results. GNNs are deep learning models that can process graph-structured data, with minimal information loss, and have been applied on a wide variety of biological tasks. Our experimental results confirm the advantage of using relationships between data entities, suggesting interesting future developments in this scope. The experimentation also shows the importance of specific subsets of data in determining associations between drugs and side-effects.

Medical Assisted Procreation (MAP) has seen a sharp increase in demand over the past decade, due to a variety of reasons, including genetic factors, health conditions altered by stress and pollution, as well as delayed pregnancy and age-related loss of fertility.The success of MAP techniques is strongly correlated to the dexterity of a human operator, who is asked to classify and select healthy oocytes to fertilize and return to the uterus.This work describes a deep learning approach to the segmentation of oocyte images, to support operators in their selection, to improve the success probability of MAP.

Rare diseases affect a growing number of individuals. One key problem for patients and their caregivers is the difficulty in reaching experts and associations competent on a particular disease. As a consequence, caregivers, often family members of the patient, learn much about the disease from their own experience. CaregiverMatcher is a proof of concept providing a smart solution to build a network of caregivers, linked by a matching mechanism based on graph neural networks. The caregivers and their experience with rare diseases are described by node features. Associations and care centers are invited to share their knowledge on the platform.

Anisoptera are a suborder of insects belonging to the order of Odonata, commonly identified with the generic term dragonflies. They are characterized by a long and thin abdomen, two large eyes, and two pairs of transparent wings. Their ability to move the four wings independently allows dragonflies to fly forwards, backwards, to stop suddenly and to hover in mid–air, as well as to achieve high flight performance, with speed up to 50 km per hour. Thanks to these particular skills, many studies have been conducted on dragonflies, also using machine learning techniques. Some analyze the muscular movements of the flight to simulate dragonflies as accurately as possible, while others try to reproduce the neuronal mechanisms of hunting dragonflies. The lack of a consistent database and the difficulties in creating valid tools for such complex tasks have significantly limited the progress in the study of dragonflies. We provide two valuable results in this context: first, a dataset of carefully selected, pre–processed and labeled images, extracted from videos, has been released; then some deep neural network models, namely CNNs and LSTMs, have been trained to accurately distinguish the different phases of dragonfly flight, with very promising results.