Publications

The boundary between AI-generated images and real photographs is becoming increasingly narrow, thanks to the realism provided by contemporary generative models. Such technological progress necessitates the evolution of existing deepfake detection algorithms to counter new threats and protect the integrity of perceived reality. Although the prevailing approach among deepfake detection methodologies relies on large collections of generated and real data, the efficacy of these methods in adapting to scenarios characterized by data scarcity remains uncertain. This obstacle arises due to the introduction of novel generation algorithms and proprietary generative models that impose restrictions on access to large-scale datasets, thereby constraining the availability of generated images. In this paper, we first analyze how the performance of current deepfake methodologies, based on the CLIP embedding space, adapt in a few-shot situation over four state-of-the-art generators. Being the CLIP embedding space not specifically tailored for the task, a fine-tuning stage is desirable, although the amount of data needed is often unavailable in a data scarcity scenario. To address this issue and limit possible overfitting, we introduce a novel approach through the Low-Rank Adaptation (LoRA) of the CLIP architecture, tailored for few-shot deepfake detection scenarios. Remarkably, the LoRA-modified CLIP, even when fine-tuned with merely 50 pairs of real and fake images, surpasses the performance of all evaluated deepfake detection models across the tested generators. Additionally, when LoRA CLIP is benchmarked against other models trained on 1,000 samples and evaluated on generative models not seen during training it exhibits superior generalization capabilities.

This article presents FRCSyn-onGoing, an ongoing challenge for face recognition where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases and standard experimental protocols. FRCSyn-onGoing is based on the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first face recognition international challenge aiming to explore the use of real and synthetic data independently, and also their fusion, in order to address existing limitations in the technology. Specifically, FRCSyn-onGoing targets concerns related to data privacy issues, demographic biases, generalization to unseen scenarios, and performance limitations in challenging scenarios, including significant age disparities between enrollment and testing, pose variations, and occlusions. To enhance face recognition performance, FRCSyn-onGoing strongly advocates for information fusion at various levels, starting from the input data, where a mix of real and synthetic domains is proposed for specific tasks of the challenge. Additionally, participating teams are allowed to fuse diverse networks within their proposed systems to improve the performance. In this article, we provide a comprehensive evaluation of the face recognition systems and results achieved so far in FRCSyn-onGoing. The results obtained in FRCSyn-onGoing, together with the proposed public ongoing benchmark, contribute significantly to the application of synthetic data to improve face recognition technology.

Fine-tuning image captioning models with hand-crafted rewards like the CIDEr metric has been a classical strategy for promoting caption quality at the sequence level. This approach, however, is known to limit descriptiveness and semantic richness and tends to drive the model towards the style of ground-truth sentences, thus losing detail and specificity. On the contrary, recent attempts to employ image-text models like CLIP as reward have led to grammatically incorrect and repetitive captions. In this paper, we propose Self-Cap a captioning approach that relies on a learnable reward model based on self-generated negatives that can discriminate captions based on their consistency with the image. Specifically, our discriminator is a fine-tuned contrastive image-text model trained to promote caption correctness while avoiding the aberrations that typically happen when training with a CLIP-based reward. To this end, our discriminator directly incorporates negative samples from a frozen captioner, which significantly improves the quality and richness of the generated captions but also reduces the fine-tuning time in comparison to using the CIDEr score as the sole metric for optimization. Experimental results demonstrate the effectiveness of our training strategy on both standard and zero-shot image captioning datasets.

Following the spread of digital channels for everyday activities and electronic payments, huge collections of online transactions are available from financial institutions. These transactions are usually organized as time series, i.e., a time-dependent sequence of tabular data, where each element of the series is a collection of heterogeneous fields (e.g., dates, amounts, categories, etc.). Transactions are usually evaluated by automated or semi-automated procedures to address financial tasks and gain insights into customers’ behavior. In the last years, many Trees-based Machine Learning methods (e.g., RandomForest, XGBoost) have been proposed for financial tasks, but they do not fully exploit in an end-to-end pipeline all the information richness of individual transactions, neither they fully model the underling temporal patterns. Instead, Deep Learning approaches have proven to be very effective in modeling complex data by representing them in a semantic latent space. In this paper, inspired by the multi-modal Deep Learning approaches used in Computer Vision and NLP, we propose UniTTab, an end-to-end Deep Learning Transformer model for transactional time series which can uniformly represent heterogeneous time-dependent data in a single embedding. Given the availability of large sets of tabular transactions, UniTTab defines a pre-training self-supervised phase to learn useful representations which can be employed to solve financial tasks such as churn prediction and loan default prediction. A strength of UniTTab is its flexibility since it can be adopted to represent time series of arbitrary length and composed of different data types in the fields. The flexibility of our model in solving different types of tasks (e.g., detection, classification, regression) and the possibility of varying the length of the input time series, from a few to hundreds of transactions, makes UniTTab a general-purpose Transformer architecture for bank transactions.

This paper presents a novel approach for identifying the dynamic parameters of a 6 DoF serial manipulator characterized by coupling and springs, which is a common mechanics for industrial robots. The proposed method consists of two steps: at first, a static identification process for estimating the masses and centers of gravity (CoGs) of the links is performed; then, a dynamic identification process for determining the inertias, motor inertias, and frictions is executed. In the dynamic identification process, a trajectory is used to generate the required dynamic response of the system, and a regression matrix is employed to combine the identified parameters. Finally, a constrained optimization method is utilized to extract the parameters. The proposed method has been validated through simulations and experiments, showing high accuracy and reliability. This research contributes to the advancement of robot modeling and control, and has potential applications in various industrial fields.

Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language. While these models have numerous benefits across various sectors, they have also raised concerns about the potential misuse of fake images and cast new pressures on fake image detection. In this work, we pioneer a systematic study on deepfake detection generated by state-of-the-art diffusion models. Firstly, we conduct a comprehensive analysis of the performance of contrastive and classification-based visual features, respectively, extracted from CLIP-based models and ResNet or Vision Transformer (ViT)-based architectures trained on image classification datasets. Our results demonstrate that fake images share common low-level cues, which render them easily recognizable. Further, we devise a multimodal setting wherein fake images are synthesized by different textual captions, which are used as seeds for a generator. Under this setting, we quantify the performance of fake detection strategies and introduce a contrastive-based disentangling method that lets us analyze the role of the semantics of textual descriptions and low-level perceptual cues. Finally, we release a new dataset, called COCOFake, containing about 1.2 million images generated from the original COCO image–caption pairs using two recent text-to-image diffusion models, namely Stable Diffusion v1.4 and v2.0.

Nowadays, deep learning models have reached incredible performance in the task of image generation. Plenty of literature works address the task of face generation and editing, with human and automatic systems that struggle to distinguish what's real from generated. Whereas most systems reached excellent visual generation quality, they still face difficulties in preserving the identity of the starting input subject. Among all the explored techniques, Semantic Image Synthesis (SIS) methods, whose goal is to generate an image conditioned on a semantic segmentation mask, are the most promising, even though preserving the perceived identity of the input subject is not their main concern. Therefore, in this paper, we investigate the problem of identity preservation in face image generation and present an SIS architecture that exploits a cross-attention mechanism to merge identity, style, and semantic features to generate faces whose identities are as similar as possible to the input ones. Experimental results reveal that the proposed method is not only suitable for preserving the identity but is also effective in the face recognition adversarial attack, i.e. hiding a second identity in the generated faces.

MicroRNAs (miRNAs) are small molecules that play an essential role in regulating gene expression by post-transcriptional gene silencing. Their study is crucial in revealing the fundamental processes underlying pathologies and, in particular, cancer. To date, most studies on miRNA regulation consider the effect of specific miRNAs on specific target mRNAs, providing wet-lab validation. However, few tools have been developed to explain the miRNAmediated regulation at the protein level. In this paper, the MoPC computational tool is presented, that relies on the partial correlation between mRNAs and proteins conditioned on the miRNA expression to predict miRNA-target interactions in multi-omic datasets. MoPC returns the list of significant miRNA-target interactions and plot the significant correlations on the heatmap in which the miRNAs and targets are ordered by the chromosomal location. The software was applied on three TCGA/CPTAC datasets (breast, glioblastoma, and lung cancer), returning enriched results in three independent targets databases.